Computer-Assisted Analysis of Modern Greek Poetry

COMPUTER-ASSISTED ANALYSIS OF MODERN GREEK POETRY

by Maria Charitou

Student number: 11103701

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Media Studies: New Media and Digital Culture at The University of Amsterdam June 2016

Supervisor: Prof. Dr. K.H. (Karina) van Dalen-Oskam

Second Reader: Prof. Dr. R.A. Rogers

To Vasiliki, for her encouragement and support.

Acknowledgements

I would like to express my gratitude to my supervisor Karina van Dalen-Oskam, Professor of Computational Literary Studies at the University of Amsterdam for the useful comments, remarks and engagement through the learning process of this master thesis. I would also like to acknowledge Richard Rogers, Professor of New Media and Digital Culture at the University of Amsterdam as the second reader of this thesis, and I am gratefully indebted for his very valuable comments on this thesis. Furthermore, I would like to thank George K. Mikros, Professor of Computational and Quantitative Linguistics at the National and Kapodistrian University of Athens, for his advice and suggestions. Overall, I would like to thank my sister Vasiliki, who has supported me throughout entire process, both by keeping me harmonious and helping me putting pieces together.

Table of Contents

1. Introduction ...... 6 1.1 Digital world and the Digital Humanities ...... 6 1.2 Digital Literary Studies ...... 13 1.3 Literary Text Analysis: Towards a Definition of ‘Style’...... 15 1.4 Computational Analysis of Poetry ...... 17 2. Research Question ...... 19 3. Methodology ...... 21 3.1 Corpus of texts ...... 21 3.1.1 Corpus preparation ...... 25 3.1.2 Modern Greek Language ...... 26 3.1.3 Poetic Generation: Definition and Periodization ...... 27 3.2 Software tools ...... 29 3.3 Stylistic Analysis: Style Markers and Text Collection ...... 31 4. Findings ...... 36 4.1 Most Frequent Words (MFW) ...... 38 4.1.1 Results on the whole corpus of texts ...... 38 4.1.2 Word-Frequency Results by Generations ...... 42 4.1.2.1 Conclusion ...... 51 4.1.3 Results by Gender ...... 52 4.2 Conclusion ...... 62 5. Literary Names ...... 63 5.1 Introduction ...... 63 5.2 Names in Modern Greek Poetry ...... 65 5.2.1 Methodology and Findings ...... 65 5.3 Discussion: Function of Names ...... 69 6. Conclusion ...... 73 6.1 Aim ...... 73 6.2 Suggestion for Future Research ...... 74 Bibliography ...... 76

Abstract

The (over)abundance of information and the ability to have access on it, the digital revolution and the disposal of digital tools we tend to use nowadays in our daily life for academic, for research or other purposes, seem to mark a new era for Science, and especially for the Humanities. From ‘Humanities Computing’ to ‘e-Humanities’ and then to ‘Digital humanities’, this new interdisciplinary field of studies promises to provide new possibilities regarding the production of knowledge. Especially, knowledge is guided through a new form of science, the so called ‘data-driven’ science, once machines and algorithms are being used increasingly in order to study (literary) texts. This essay examines the possibilities given by the use of software tools and investigates how quantitative analysis can contribute significantly to the study of poetic texts, radically altering the way on which Humanities are being understood and practiced. Therefore, it mentions a number of significant debates on the field showing its vigorous effect, and makes a particular reference on literary studies and the challenge they face by the implementation of algorithms. Focusing on Stylometry and Onomastic studies, this study aims to analyze Modern Greek poetry with the use of digital Stylistic tools and to investigate how Proper Names operate in poetry. Overall, it aims to explore the role of computers for the analysis of Modern Greek poetry.

Keywords

Digital Humanities, Digital Literary Studies, Modern Greek Poetry, Textual Analysis, Computational Stylistics, Word-Frequency, Names.

1. Introduction

1.1 Digital world and the Digital Humanities

Digital technology and the current deluge of data are shifting, amongst other things (i.e. access to information), the way research is being conducted (Kitchin 2014, 128- 148). Not only because research is increasingly being conducted through the use of digital technology, but also because with the rise of digital tools many traditional methodologies seem to have changed or tend to do so. Thus, digital technology spreads to a variety of epistemological fields and challenges traditional disciplines from the (Social) Sciences to Humanities, creating what is being called ‘Digital Humanities’. Digital Humanities is an “emerging” interdisciplinary field of studies that intersects Computer Science and Humanities and examines the way Humanities deal with (digital) technology, (new) media and digital methods (Svensson 2010; Arthur and Bode 2014). The change of the term, from ‘Humanities computing’ to ‘Digital Humanities’, signifies the progressive development of the field as it “emerged from the low-prestige status of a support service into a genuinely intellectual endeavor with its own professional practices, rigorous standards, and exciting theoretical explorations” (Hayles 2012b, 43). Particularly, digital tools such as text mining, machine learning, data mining, data visualization, information retrieval, etc. merge with traditional Humanities practices, such as Hermeneutics, close reading, philology, archiving, critical textual or visual analysis, text analysis etc. The availability of tools, algorithms, databases, interfaces, software etc. suggests a different analysis from the traditional one, “focused on the finding of patterns, dynamics, and relationships in data” (Rieder and Röhle 2012, 70), and demonstrate the shift from micro to macro analysis (Jockers 2013). Subsequently, this ‘digital diffusion’ places in a new frame the production of knowledge and has both epistemological and ontological repercussions: not only because digital tools, platforms, (computational) techniques, and (new) media have significant consequences on the production and dissemination of knowledge in the

Humanities, but also because they “represent new possibilities to study human interaction and imagination on a very large scale” (Rieder and Röhle 2012, 68). More precisely, it appears that digital technology affects the way we think and how we think about thinking, regarding the fact that nowadays we tend to think “through, with, and alongside media” (Hayles 2012a, 1). Moreover, it has implications on how we conceive of universities and of learning processes -by the application of new pedagogical practices in classrooms and new research methods. But whereas the rise of Digital Humanities is said to have brought a “new lustre to a tired field” (Kitchin 2014, 141), they have been criticized of leaving behind old research methods –such as hermeneutics, semiotics and searching on library catalogues and archives collections–, thus giving rise to significant debates. Stanley Fish and Stephen Ramsay, amongst others, disputed strongly on whether literary criticism can be assisted by computer algorithms providing data ready to analyze (data-driven analysis), or, whether the criticism that derives from close reading of a text is unique and therefore irreplaceable (Fish 2012; Ramsay 2012). Gardiner and Musto while trying to define Digital Humanities, they pose a number of questions related to the scope and features that differentiate Digital Humanities from the Humanities. Amongst other questions the one that wonders about the significance of the digital seems to be crucial: “has the arrival of the digital forever changed the way humanists work, in the way they gather data and evidence or even in the very questions that humanists and the humanistic disciplines are now capable of posing?” (Gardiner and Musto 2015, 2). More precisely, the growing use of digital tools in Humanities signify what Berry calls “computational turn” (2011, 11) in the Humanities and promotes new ways of gathering information, sharing and/or analyzing texts and data, teaching, and/or publishing. This “computational turn” though, has opportunities as well as challenges and tends to divide scholars into opponents and advocates, causing debates in the field (Gold 2012). Especially, there are those believing that the digital world is radically changing the way research is being conducted and those thinking that the digital simply helps the work of the scholar or even that it might undermine the core of it: “many humanists tend to view the digital humanities as a methodology that brings the tools and power of computing to bear on the traditional work of the humanities.” (Gardiner and Musto 2015, 3).

To some, it sees that digital tools give the opportunity to a scholar to conduct his/her research by spending less time and effort: (s)he now has at his/her disposal more sources (digital libraries; repositories; digitized manuscripts; Google books; Project Gutenberg etc.), that offer easy and quick access, regardless of scholar’s geographical position, and -last but not least- they are less costly (Gardiner and Musto 2015). Regarding that, it is worth mentioning that Digital Humanities comes at a time, when Humanities are dealing with budget cuts and scepticism about their value (Liu and Thomas 2015, 35). Specifically, humanities today seem to be “the victim of a perfect storm” and “budget cuts stemming from a persistent recession [...] have threatened humanities programs everywhere” (Jay 2014, 1). At the same time, Digital Humanities “experienced a banner year that saw cluster hires at multiple universities, the establishment of new digital humanities centers and initiatives across the globe, and multimillion-dollar grants distributed by federal agencies and charitable foundations.” (Gold 2012, ix). Despite its financial aspect and general boost, Digital Humanities suggest a new approach to Humanities methodology. Computer and software tools in general can assist into giving different perspectives and aspects, “alternative visions” as Ramsay argues, when it comes, for example, to an analysis or interpretation of a text (Ramsay 2011, 16). In other words, software, as Berry declares, “allows for new ways of reading and writing” (2012, 13-14). By referring to Gertrude Stein’s book The Making of Americans, Berry points out that an analysis of such impenetrable texts can be facilitated through text mining (Berry 2012, 13). More specifically, the reading and interpretation process of The Making of Americans, a text “almost impossible to read it in a traditional, linear manner” (Clement 2008, 362) because of its structure, was facilitated through various computational techniques, such as the visualization of certain patterns. As Clement argues: “by visualizing certain patterns and looking at the text ‘from a distance’ through textual analytics and visualizations, we are enabled to make readings that were formerly inhibited. […] Using text mining to retrieve repetitive patterns and treating each as a single object makes it possible to visualize and compare the three dimensions upon which these repetitions co-occur—by length, frequency, and location—in a single view.” (2008, 361). That clarifies that in some cases algorithms may lead to a different perspective of a text and/or allow to have different kinds of knowledge about a text –such as patterns, trends, correlations, quantities or frequencies of particular words and phrases.

In addition, visualization and data mining (graphs, maps etc.) can be used in order to do ‘distance readings’ of literary books: “where ‘distance’ is not an obstacle, but a specific form of knowledge” (Moretti 2005, 1). Where traditional humanities base their research method on close reading, digital humanities introduce distant readings of texts. As Moretti declares, traditional humanities focuses on a “minimal fraction of the literary field [...] canon of two hundred novels, for instance, sounds very large for nineteenth-century Britain (and is much larger than the current one), but is still less than one per cent of the novels that were actually published: twenty thousand, thirty, more, no one really knows – and close reading won’t help here, a novel a day every day of the year would take a century or so. .. And it’s not even a matter of time, but of method: a field this large cannot be understood by stitching together separate bits of knowledge about individual cases, because it isn’t a sum of individual cases: it’s a collective system that should be grasped as such, as a whole.” (2007, 3-4). Moretti speaks about a transition from texts to models, where computers can operate as, what Ramsay calls, “reading machines” (2011). But, what does “reading” as a process of interpretation stands for and what kind of readings do we have when texts are being treated or studied through algorithms/machines? Briefly referring to the concept of the term, ‘Hermeneutics’ perceive ‘reading’ as a process in which the meaning of a text is being recovered by an attentive reader via the act of ‘interpretation’ (New Princeton Encyclopedia 1993, 516-520). Reading as a knowledge-producing process aims to unfold the meanings of a text, those that lie behind or within it. Kittler pointed on the transition from the ‘writing world’ to automation and machines, and particularly on how new media changed knowledge-producing processes, such as writing and reading (Kittler 1999). Therefore, ‘reading’ is a process that needs a reciprocal action between the text and the reader, where the reader interacts with the text (Kittler), whilst in the context of Digital Humanities ‘reading’ is a process of “becoming integrated with the text” (Evans and Rees 2012, 27). A distinction between types of readings –close reading (the one that “correlates with deep attention”), machine reading (“an analysis through machine algorithms”) and hyper reading (“often associated with reading on the web”) – is pointed out by Hayles who asserts that within the Humanities “reading connotes sophisticated interpretations achieved through long years of scholarly study and immersion in primary texts” (2012a, 11-12). On the other hand, Hayles continues,

“‘reading’ implies a model that eschews human interpretation for algorithms employing a minimum of assumptions about what results will prove interesting or important” (2012a, 8). It is true that with digital technology the reading process seems to have changed: especially because it is now being done through and with search engines, and via the opportunities that Internet provides, thus producing a type of reading that ‘scans’ (reading as an act of scanning) due to the fast and huge diffusion of information. Along with Hayles, Evans and Rees argue that what have changed are the methodology and the results along with the subject and fields of research; thus the desire for the acquisition of knowledge still remains, whether Humanities are called digital or traditional (2012, 31). That the use of computers and particularly of digital tools brings out a new methodology to Humanities has been pointed out by demonstrating the significant facility of them on the procedure of interpretation. Either the close reader (i.e. the scholar etc.) or the computer, each one contributes differently to the interpretation of a text. As Burrows declares, “the close reader sees things in a text […] to which computer programs give no easy access. The computer, on the other hand, reveals hidden patterns and enables us to marshal hosts of instances too numerous for our unassisted powers” (Burrows 2002, 696). Ramsay’s proposal of an ‘algorithmic criticism’ acts as a new way to incorporate digital tools in Humanities research as they allow “critical engagement, interpretation, conversation, and contemplation” but also that via those tools “we channel the heightened objectivity made possible by the machine into the cultivation of those heightened subjectivities necessary for critical work” (Ramsay 2011, x). In other words, close reading of traditional Humanities is related to the subjective gaze of the scholar, while Digital Humanities can be objective because of the tools they are bringing (for example, coding). A related point to consider though is that Ramsay does not reduce the value of subjectivity or subjective judgement when it comes to literary criticism: “Literary- critical interpretation is not just a qualitative matter; it is also an insistently subjective manner of engagement” (2011, 8). He therefore asserts that the patterns (data) a critic gets through a machine (computer) can be used for “grander rhetorical formations that constitute critical reading” (2011, 17) and he uses the term ‘algorithmic criticism’ to determine a literary criticism assisted by computers (2011, 32).

Furthermore, macro-analysis focuses on the analysis of concepts and features at the macro-level. This new approach points out the significance and potential of computational analysis for the interpretation of literary texts in a large scale. Jockers (2013) uses the word analysis instead of reading, thus emphasizing on the examination of data and the importance of computers, which can do large-scale analysis faster than humans. He tries to overcome the boundaries and possibilities of close-reading’s literary interpretations, by outlining the way this method can work in order to understand the individual works into large digital collections: for example by using topic models, relative work frequencies, stylometrics and statistical methods. Digital tools might promote a literary research without even reading a text, based on the difference between close (human-based) and ‘distant reading’ (computer-based) (Culler 2010). “Literature cannot meaningfully be treated as data” Marche states (2012), because literary texts are more than just words or lyrics: as they constitute feeling expressions and/or reveal thoughts and opinions of the writer, algorithms are incapable of indicating the meaning behind the words. Subsequently, Marche argues (2012) that “the process of turning literature into data removes distinction itself. It removes taste”. More concretely, ‘taste’ constitutes a fundamental element of culture, enabling to “sense or intuit what is likely (or unlikely) to befall […] an individual occupying a given position in social space. It [i.e. taste] functions as a sort of social orientation, a ‘sense of one’s place’” (Bourdieu 1984, 484-85). In terms of this, ‘taste’ seems to include more or less a subjective gaze, which tends to be lost, according to Marche, when algorithms are being used. But how objective can a machine be? And do we really lose ‘taste’? Before examining the extent to which objectivity is possible or real when it comes to algorithms, allow me to refer again to Marche for his controversy is worth mentioning. He refers to algorithms as “inherently fascistic, because they give the comforting illusion of an alterity to human affairs. “You don’t like this music? The algorithms have worked it out” is not so far from “You don’t like this law? It works objectively.” Algorithms have replaced laws of human nature, the vital distinction being that nobody can read them. They describe human meanings but are meaningless”. An algorithm can decide for us or even play an auxiliary role in our decisions only if we allow it to do that. In other words, machines and computers are being used and -in a sense manipulated- by people who decide what to exclude and what to

include when coding, programming and/or analyzing (textual) data. In that sense there is not enough space for objectivity as we would like to pursue when it comes to algorithms and further on to Digital Humanities. Especially, objectivity constitutes one challenge among the five ones that Rieder and Röhle (2012) present while questioning the growing use of digital tools in the Humanities: objectivity, visualisation, ‘black-boxing’, institutions and universalism (i.e. the desire to universality). While the main aim of Humanities’ research when using digital tools is objectivity, this cannot be entirely achieved. Machines might easily be used for a variety of tasks enabling research by overcoming any obstacles, but “they should not be taken to guarantee a higher epistemological status of the results” (Rieder and Röhle 2012, 73), as they might cause more complexities than unravel. Although machines seem to diminish or even to eliminate any human errors and/or subjective judgement, there are elements whenever an analysis takes place, such as selection and modelling, that are based upon subjective criteria and therefore this cannot be an objective procedure (Evans and Rees 2012, 27). Moreover, the advent of visualisation tools has enabled the Humanities to present their research results in a different way, via images, graphs etc. Although visualisation as a procedure comes up with advantages, it also reveals drawbacks regarding the cogency of images: “their imagined link to an external reality, and the obscurity of their production process. Both problems can be addressed to some extent by drawing on the tradition of critical enquiry into the use of images that the humanities have fostered over the years” (Rieder and Röhle 2012, 75). Along with the desire of the Humanities to be universal, Rieder and Röhle point out that despite the “practical need to formalize contents and practices into data structures, algorithms, modes of representation, and possibilities for interaction” that practicality does not make “the methodological procedures more transparent” (2012, 75). In terms of this, the interpretation of a text can -in a way- co-produced both by humans and machines. Digital technology assists today’s Humanities scholar by providing him useful tools and allowing him to have open (and sometimes free) access to literary works, books and scientific articles that huge databases include. By retrieving key words, phrases, titles and linguistic patterns located into thousands of literary texts in repositories and digital libraries, a researcher can find correlations between authors and genres, highlighting any linkages between cultures, and

investigate literary tendencies and movements of the past between different places and time periods. An algorithm can provide us observations and/or interpretations that might otherwise not be possible to have (Moretti 2007), but on the contrary there are a number of limitations on what a computer or algorithm can accomplish. Barocas and Selbst demonstrate that “an algorithm is only as good as the data it works with” (2015, 1), meaning that algorithms and their results depend on the type of data each time the scholar applies. Thus, coding and computers might broaden the act of criticism, but they cannot substitute it. They can only play a supporting role by assisting the scholar in his/her research: “The computer’s role is only to ask how our engagements might be facilitated, but it does so with a staggering range of provisos and conditions” (Ramsay 2011, 66).

1.2 Digital Literary Studies

In this Digital Humanities era where the presence of computer and tools is so concrete, hegemonic and inevitable that it leads towards a ‘Post Digital Humanities’ era as Berry argues (2014), Literary studies are facing a great challenge and experience a significant gradual change. Hyper reading, e-Philology, digital literature, digital archives, computer-assisted analysis, digital editing, etc. are some of the consequences that take place when Literature encounters new media and digital tools. More precisely, Digital Literary studies constitute a branch of Digital Humanities that examine the possibilities and application of digital methods, practices and (software) tools in the field of literary studies (computational literary analysis: macro analysis, data mining, distant reading, topic modelling, visualisation etc). Digital humanities and digital literary studies “are concerned with reading practices, the creation of scholarship, the representation of knowledge, and the refinement and expansion of methodologies of interpretation—all undertaken [...] in a computer- assisted environment.” (Price and Siemens 2013). The scholar nowadays can be assisted by various visualisation tools that aim to depict data derived from large literary corpora. As Price and Siemens argue, “digital scholarship is remaking literary

studies, creating new lines of inquiry, reshaping our fundamental practices, and, in doing so, laying the foundation for a future in our field where computation (use of a computer) is an assumed, rather than a notably innovative or particularly remarkable, method of literary pursuit.” (2013). In other words, Digital Literary studies introduce a variety of methodologies that contributes to an analysis of texts (stylistic, thematic analysis etc.) and interpretation supported by digital technology, by computers and algorithms. They signify amongst others a transition from close reading to machine and hyper reading, i.e. from reading papers and/or books to texts readable by computers. Amongst other things, Digital Literary studies concentrate on “style, diction, characterization, and interpretation” of either one literary text or across several groups of texts and apply “statistical techniques used in the narrower confines of authorship attribution to broader stylistic questions” (Hoover et al. 2014, 2). This quantitative approach to literary texts does not only focus on style but also to the research of issues such as genre, themes, characters etc.: text analysis programs have been developed to be employed for the study of various parts of speech (noun, adverb, preposition etc.), discrete themes, named entities (personal names, toponymy, etc.), sentiment, and meter (Jockers 2013, 16). In other words, computational text analysis focuses on stylistic analysis, stylometry, authorship attribution (i.e. the attempt to find the author of an unauthorized text) and intersects with natural language processing, artificial intelligence, data mining etc. Stylistic studies, in particular, focus on examining and describing an author’s style (usually distinctive) and/or compare and contrast it with the style of one or more other authors. This comparison is based on the same language of written texts, genre, historical period etc. (Hoover et al. 2014, 90). In authorship attribution, for example, software tools have been employed to mark choice of words and measure letter and punctuation frequencies or function words and sentence structure, as they also constitute part of a writer’s style, in order to identify the author of a text. More recently, a visualization of eight classic fiction books was based on punctuation use, marking the differences between the authors (Calhoun 2016). Moreover, in the context of text analysis, word frequencies and syntactic phenomena are being counted, such as word classes (noun, verb, prepositions, adjectives etc.) and phrasal categories, as well as grammatical and semantic categories (tense, number, gender; themes derived from the words, i.e. emotions words). N-

grams (sequences of words or letters), collocations (words that appear close to other words), sentences and phrases can also be counted as well as the length of various elements, such as word length (characters/letters), sentence length (the number of words per line, syllables or characters per sentence or clause) and of course the length of a text (the total number of words, sentences, punctuations, paragraphs, stanzas, chapters etc.). Apart from these linguistic features, it can also measure literary aspects, like motifs, characters, themes (death, war, fashion etc.) and names (Hoover 2008). In terms of this, comparisons can be made by examining different authors (ancestors, predecessors, contemporary), texts from different time periods and gender, different works or passages of the same author, translations of the same work etc.

1.3 Literary Text Analysis: Towards a Definition of ‘Style’

But what does ‘style’ stand for in a literary context? Various scholars have analysed its significance for literary studies and suggested a number of definitions throughout the years. This plurality of definitions comes from the different perspectives of ‘style’, either seen linguistically or from a Digital Humanities perspective. Firstly, The New Princeton Encyclopedia of Poetry and Poetics (1993, 1225) connects the word ‘style’ to an author's individuality, his/her choice of words and collocations that construct the meaning of a text. Its feature of distinctiveness is also pointed out by Wales who defines ‘style’ as “the perceived distinctive manner of expression” (2001, 371). From a linguistic perspective, ‘style’ refers to a specific use of language to a specific context by a specific writer/speaker, in a particular time period. Subsequently, the term ‘style’ often signifies particular linguistic choices of an author (syntactic, lexical, morphological or other preferences or choices), which characterize/formulate his/her unique writing style (for example, scholars have analysed the style of T.S. Eliot, of Shakespeare etc.). Writers such as Leech and Short consider ‘style’ as “the way language is used in a particular genre, period, school of writing or some combination of these: ‘epistolary style’, ‘early eighteenth-century style’, ‘euphuistic style’1, ‘the style of Victorian novels’, etc.” (Leech & Short 2007,

10). Leech and Short speak about a “linguistic ‘thumbprint’”, a stylistic fingerprint that declares the identity of the writer based upon this “individual combination of linguistic habits which somehow betrays him in all that he writes” (2007, 10). That dominance of the linguistic habits which identify the ‘style’ of one author is also present to its definition from a Digital Humanities perspective. Into that context, “style is seen as anything that can be measured in the linguistic form of a text, such as vocabulary, punctuation marks, sentence length, word length, the use of character strings. [...] Every word and every feature contributes to the general outlook of the text; any other ratio in frequencies, any difference in mean sentence length, every individual punctuation use results in a different outlook of the text. In short: everything is important” (Herrmann, Van Dalen-Oskam, and Schöch 2015, 38). Herrmann, Van Dalen-Oskam, and Schöch propose a new definition of style, according to which “Style is a property of texts constituted by an ensemble of formal features which can be observed quantitatively or qualitatively” (2015, 44). Such a definition emphasizes the constitution of style through various text characteristics (levels such as syntactic, semantic, lexical etc. and sentences) and style is being viewed as a composite system: “By ‘ensemble’ we mean that style is constituted by the combination of many possible features and should be seen as a complex system, with features situated at different linguistic levels. By ‘formal features’, we mean linguistic features at the level of characters, lexicon, syntax, semantics, but also features going beyond the sentence, such as narrative perspective or textual macro- structure;” (Herrmann, Van Dalen-Oskam, and Schöch 2015, 44). By highlighting two approaches into the study of style, the quantitative and the qualitative, Herrmann, Van Dalen-Oskam, and Schöch refer to a specific style that can be studied with computational methods “based on computing frequencies, relations, and distributions of features and relevant statistics, as well as methods based on precise observation and description of individual occurrences.” (2015, 45). Subsequently, the study of ‘style’ and of the particular use of language is called Stylistics; more accurately “a method of textual interpretation in which primacy of place is assigned to language.” The significance of language is determined by the fact that its structure is defined by various levels (syntactic, phonetic etc.) and forms that declare the function. And “the text’s functional significance as discourse acts in turn as a gateway to its interpretation” (Simpson 2004, 2). However, prose differs from poetry regarding linguistic choices and structure of the text. When someone

examines the style of a poetic text, she/he has to take into consideration various aspects of poetic levels. At the phonemic level, a poet’s style is formed by rhyme and rhythm, and sound devices, such as assonance, alliteration, consonance, resonance, repetition and meter. The syntactic level of a poem reveals stylistic options related to order of words, use of phrases, punctuation marks, the number of stanzas and lines or the number of words per line etc.; and the morphological level relates to word length, type of words (function and content words; conjunction words, nouns, adjectives, adverbs, verbs, participles etc.), and considers the variety of verb tenses and persons due to classification of pronouns, possessive determiners, and verb forms. Despite the various definitions of style that have been proposed during time, today with the advent of the digital technology, new methods of analysing texts and studying style have come into use. Computational Stylistics or Stylometry refers to the study of literary style of an author or group of texts with the use of various computational techniques and it includes studies for authorship attribution and author style. Except from the study of style in fiction, Computational Stylistics has also applied to poetry. Especially, Computational Stylistics in poetry focuses on analyzing the frequency of the above mentioned aspects of a poetic text that signify a poets’ ‘fingerprint’.

1.4 Computational Analysis of Poetry

Poems constitute complex structures, as they consist of three major levels: the phonetic and semantic level and their form. Each one of them has a range of features determining the interpretation of a poem: its form consists of stanzas, lines and lyrics; the phonetic level refers to meter, intonation, prosody, rhythm etc. and the semantic one refers to genre, words, sentences etc. Each one of these features formulates ‘style’. Regarding computational analysis, former studies on the field have proved that computational methods can contribute significantly to a better understanding of the poetic text. Specifically, computational analysis of poetry has been the main subject of a number of papers and studies from the very beginning of linguistic and literary computing (Kenny 1982; Beatie 1967) to the very recent time. In fact, previous

research on computational analysis of poetry has investigated influences between poets regarding style, theme etc. and similarities based on words and syntax (Coffee et al. 2013), and analysed patterns in poetry such as rhythm and rhyme (Plamondon 2006). Related studies have, also, focused on different aspects of the poetic texts, such as stylistic, phonetic features and authorship attribution. In particular, Kao and Jurafksy (2015) analyzed the stylistic features of English poems of 19th and 20th century and compared them by looking at sound devices, diction, words of sentiment/emotional language etc. The analysis of those poems, written by different poets -Imagist2, professional, contemporary professional, and contemporary amateur poets- signified the impact of Imagism on modern poetry and unveiled differences between “‘high’ and ‘low’ art” (1-2). Kaplan and Blei (2007) estimated the division between the style of American poets (using poems of Robert Frost, Marianne Moore and Frank O’Hara) by examining a variety of features – orthographic, syntactic and phonemic– and visualized the differences based on poets’ relation. Subsequently, Can et al. (2014) used computational methods by applying Automatic Text Categorization methods (ATC) to Ottoman poetic texts in order to classify a text written by an unknown poet or unknown time period. Moreover, Brooke et al. (2012; 2015) studied stylistic inconsistency and heterogeneity in The Waste Land, a poem written by T.S. Eliot, and known, amongst others, for the different voices it contains. They proved that computational stylistic analysis can be helpful in the identification of distinctive voices into a poetic text. Arefin et al. (2014) also examined word frequencies of texts from the Shakespearean era in order to find relations between them. Their results signified the importance of authors’ style into “explaining the variation of word use”, and “the differences between tragedy and comedy, early and late works, and plays and poems”.

2. Research Question

As showed above, Computational stylistics’ methods have been broadly employed to show stylistic differentiations or identify the author of unknown texts in several different languages. Taking into consideration the possibilities that software tools can provide us with when we study literary texts, my purpose is to demonstrate their importance for the study of Modern Greek Poetry. More concretely, this study suggests a way to unravel any relations between Modern Greek poets based on computational stylistics by focusing on analyzing word frequencies and named entities (name types and functions of names). I expect that my results will allow me to make concrete comparisons across different poets and time periods. In terms of this, my main research question is: what do frequencies of semantic categories/words unfold for Modern Greek Poetry of 19th and 20th century and generally for Modern Greek Poetic Language? In achieving this, I will focus on the analysis of nouns and try to find what their frequencies reveal for Modern Greek poetry in general and for each Modern Greek poetic generation. I will, subsequently, search if these words are affected by literary period and whether and to what extent they reflect gender. The second part of this study will focus on names, their types, frequency and function. For my sub-research question -to what extent the use of named entities reveal relations between poets? - I will try to study names both quantitatively and qualitatively. To the best of my knowledge nothing relevant has been studied before regarding Modern Greek literary texts and precisely Modern Greek poetry by using computational techniques, methods, and statistical analysis. Regarding computational stylistic analysis though, Pantopoulos (2012) examined the prominent stylistic features and word frequencies of three translators (Rae Dalven, Edmund Keeley and Philip Sherrard) of C.P. Cavafy’s canon poems and pointed out the difference in stylistic choices between the translators. Several Modern Greek studies have examined the relations between Greek poetic generations in an attempt to reveal influences and/or intertexts between predecessors and successors (Pylarinos 2009; Garantoudis 1999). These influences and intertexts occur in diverse levels of poems, such as themes, style, use of myth, form of the poem etc. (i.e. rewriting of forms,

myths, themes etc.) and might occur between Modern Greek and non Greek poets. In particular, Malli (2002) examined the extent on which the ‘Generation of the 70s’ has been influenced by American Beat poetry and Imagism. Although studies have examined the style of specific poets and/or generations (Malli 2002), it has not been published so far a study referring to stylistic analysis and comparison of several poetic generations simultaneously or a stylistic analysis focused on nouns used by poets belonging to a large poetic range (from 1821 and onwards). Regarding names and their appearance on Modern Greek poetry of 19th and 20th century, relevant studies have not yet published. Although scholars studied the influence of a specific myth and mythical characters, such as Ikaros, Elpenor and Odysseus, in various Modern Greek poets (Savvides 1981; Oikonomou 2004; Katsigianni 1999) and how these are being re-presented, it still misses a study that will examine named entities in Modern Greek poetry, their function and difference between diverse generations. By comparing the frequency and function on different poetic generations, I hope to show the range of use on name types per generations, their function and difference. Therefore, the following chapters aim to fill in this gap and contribute to the studies for Modern Greek Poetic Language.

3. Methodology

3.1 Corpus of texts

The first step of my study was to determine the corpus of texts to be investigated. Since a limited percentage of Modern Greek poetic texts are available online, in digital form and freely accessible -mainly through the Digital Library of Modern Greek studies, the Anemi- the possibility of digitizing a certain amount of poetic collections or use OCR to already digitized books was rejected due to lack of time (digitization is a harsh and time consuming process). Therefore, I chose to work with already digitally/electronically existing poetic texts that could be found online. I begun with a Web search in order to find and determine my set of electronic texts. My Web search located specific poetic anthologies that host texts from the 18th to the 19th century. Finding electronic Modern Greek poetic texts proved to be a harsh procedure: Project Gutenberg does not contain enough Modern Greek poems; Internet archive includes poems or anthologies not published during 19th century and moreover the number of books is limited (I got 28 results by searching the term ‘Modern Greek Poetry’ in Google); and the majority of Google Books related to Modern Greek Poetry are available only for preview, due to copyright issues. There are also a significant number of other online anthologies (such as ‘Portal for the Greek Language’ and ‘Myriobiblos’), but either their content was similar to the two online anthologies I finally chose, or, the collection of poems was not large enough or contained poets not belonging to those poetic generations I was looking for (mainly the newest generations). Moreover, the number of poetic texts in electronic form varies (per poet, poetic generation, and gender), and usually the sites or databases that publish those poems are not official (blogs, online journals etc.), or, contain no information about the print source of poetic collections. Moreover, one concrete factor determining where to look for and get my set of electronic texts was their copyright license. Thus, before selecting the texts, I had to be sure that I could use them for my research and I could have information about their print source.

The next step was to create a corpus of poetic texts by retrieving them from specific official websites or databases in order to have a representative sample of Modern Greek poems. Therefore, Snhell.gr and Ekebi.gr were chosen as my main resources, not only because they both constitute official databases/archives and contain anthologies of Modern Greek poems, but also because they mention the print source of each poem (although Ekebi does not follow that for every single poem). Subsequently, I contacted the creators of Snhell.gr, and Ekebi.gr to have their permission before using their online anthologies. The website ‘Spoudasterio Neou Hellenismou’, hence the acronym ‘Snhell’, was conceived and created by the Center for Neo-Hellenic Studies in Athens, Greece. It is edited in Modern Greek and contains 856 Modern Greek poetic and prose texts available in electronic form3. In total the database contains 101 Greek writers and the texts are derived from various historical periods: before World War I, between World War I and II, and after World War II. It is worth mentioning that the database is constantly enriching its content and, besides the collection of the above mentioned poems, it also includes prose texts, anthologies of testimonies, of Audio Readings, of children’s tradition, and information on the work of the poet C. P. Cavafy (1863- 1933), of the historian and critic Konstantinos Th. Dimaras (1904-1992), and G.P. Savvides (1929-1995), an important scholar of modern Greek Literature.

Figure 1. Snhell: The first of the two online anthologies I chose for my corpus of texts. Its aim is to “promote Modern Greek Literature and Culture”.

Of great importance for my research is that the site declares the print source of each text. Although Snhell contains a significant number of poetic texts, it does not include enough poets from the latest generations or enough female poets. In order to have a greater variety of poems for the stylistic comparison between poets, I needed to add some female poets and include more poets from the latest poetic generations. Thus, the enlargement of the corpus of texts was done by adding poems from a second online anthology, the one published on ekebi.gr. The website ekebi.gr –an acronym that stands for the ‘National Book Centre of Greece’– contains an anthology of Modern Greek poets, mostly derived from the Post War Generation, the Generation of 70’s and 80’s. Specifically, the collection of poems made by a Greek poet himself, Dimitris Kosmopoulos, includes 510 poems and 137 Modern poets of the 19th and 20th century, both Greek and Cyprian, derived from the years of the National Greek poet Dionysios Solomos (1798-1857) until the latest poetic generations.

Figure 2: Ekebi.gr promotes reading and books and contains several digital archives.

By unifying the content of the above mentioned online anthologies, ekebi.gr and Snhell.gr, I managed to compile a corpus of poetic texts from a broad range of poets, both female and male, derived from different time periods. The final anthology provides a representative sample of Modern Greek Poetry. Subsequently, I decided to limit my corpus of poems to those written in the 19th and the 20th century; and therefore I excluded from the corpus: a) 14 poets of the

18th century and further back, mainly from snhell.gr; b) any prose texts existing in the database; specifically 14 writers that are being listed in snhell.gr; c) any poems translated by C.P. Cavafy (10 poems in total); and d) any Cyprian poets (appeared only in ekebi’s database). Thereafter, I combined the poems from the two databases and deleted any poems I encountered twice. Regarding the corpus of texts chosen for stylistic analysis, I had to exclude poets that only have 1 or 2 poems published in the anthologies (22 poems in total and 17 poets). The number of the poems is so small that cannot be representative of their poetry. However, these 15 poets were taken into consideration in the second part of this study, i.e. the study of named entities. Overall, from both online anthologies I excluded 34 poets: 28 poets mentioned on snhell, and 6 Cyprian poets whose poems are published on ekebi. Thus, for my analysis it remained a total of 153 poets and 1167 poems. The graph in figure 3 unifies the two databases and depicts the exact amount of poets I used for my study:

Figure 3: The total amount of male and female poets on both online anthologies.

From the whole set of 1167 Poems, 77 poems are written by female poets and 1090 by male poets. As can be seen from the pie chart in fig. 3, female poets are greatly underrepresented in the Greek online databases compared to male poets. It is true that there are less published female than male poets until the 19th century, a phenomenon

probably related to education and general rights of women in Greece. Published writers of that time period were mostly men. Secondly, a main characteristic of the anthologies in general is that they mostly reflect the subjective gaze of the anthologist, who based on his/her own criteria decides which poet to include and what poems represent him/her more. This is not of course to accuse anthologists for not including female poets in online collections, but rather to pinpoint a great gap on the online presentation of female poetry in Greece.

3.1.1 Corpus preparation

In order to prepare the texts for my analysis I saved them in a ‘txt. file’ and converted them into ‘UTF-8’ coding to be readable by the tools I was going to use. From the main corpus of the poems I left out the titles, any mottos and/or any comments written by the poet or copyeditor, as those elements belong to paratexts (Genette 1997). The creation of the files was based on authors and poems (each author constitutes one file containing his/her poets), on gender (files divided into female and male), and poetic generation. Also, one file was created containing all poems by all poets for an overall analysis. For the poems taken from the website ekebi.gr I had to convert the system of tones, from polytonic to monotonic, in order to have a homogeneous corpus of texts and be readable by the software tools. For the conversion procedure I used an online tool, available on translatum.gr (see fig. 4 a,b).

Figure 4 (a, b): An example of conversion from polytonic to monotonic Greek.

3.1.2 Modern Greek Language

It is useful at this part of the study to briefly mention some features of the Modern Greek Language, as we are going to refer to them in the following chapters. Modern Greek language marks its official beginning with the fail of Constantinople, the capital city of Byzantium at 1453, although some features of the modern language have existed centuries before. It followed an evolutionary process originating from Ancient Greek and Byzantine that also marks the division of Greek Literature, into Ancient, Byzantine and Modern Greek. One significant characteristic of Modern Greek was the matter of diglossia that took place during the 19th and 20th century. The term signifies a language controversy and the simultaneous use of two forms of Greek: Katharevousa and Demotic Greek. Katharevousa corresponds to a combination of Ancient and Modern Greek and was used for official purposes and in literature. It depicts the written and spoken language of the elite or well educated people of the period. On the other hand, the form Demotic referred to a daily use of Greek language that was mostly spoken by the mass. Demotic Greek became the official form of language of Greece in 1976 and in 1982 the polytonic system was removed. Katharevousa and Demotic Greek differ

not only in the tonic system but also in the syntax, both in the morphology as well as the syntactic form of language (Christidis et al. 2003). Having selected poetic texts from 19th and 20th century I encountered a significant amount of variations of words, due to the fact that Greek has declensions, i.e. variations of the form of a noun, pronoun and/or adjective. These variations identify the grammatical case, the number and the gender of the word. Moreover, I came across different forms of Greek language, types of Demotic Greek as well as Katharevousa, especially when comparing texts of 19th to 20th century. Those variations of words where combined in order to refer to one word type by ignoring/deleting the suffixes and keeping only the root of each word (for nouns, adjectives, verbs, pronouns etc.).

3.1.3 Poetic Generation: Definition and Periodization

The term generation is as a way of classifying poets into generations by using as a basic criterion their age, i.e. date of birth, and/or the year of publication of their first poem(s) (Argyriou 1979). This factor determines the social, historical and ideological environment in which they grow up and are most influenced by. Poets of the same generation usually share experiences and beliefs and they often have common literary influences as well as aesthetic orientations. This is being depicted in their poetry as they share ways of expression (vocabulary, language, style) and themes (Garantoudis 1998, 194-202). Although Vitti (1995) gave a definition of the term ‘literary generation’ referring to the ‘Generation of the 30s’, that term was widely accepted by the critics as representative (Garantoudis). According to Vitti, a ‘literary generation’ is a group of writers having innovative ambitions, desire to differentiate significantly from their predecessors and present new themes and forms based on their common experiences (1995, 53). As Garantoudis declares, the terms “Generation of 1880”, “Generation of the 1920” and “Generation of the 1930” because of their long-term use in Modern Greek Literature, they formed the basis on which it was developed a trend for systematic genealogical classification of Modern Greek poetry. Following this generally accepted classification and periodization of Modern Greek Poetry (Beaton 1996), I will present some of the main characteristics of each

generation that appears on my corpus of texts: The ‘Ionian Islands’ School’, The ‘Athenian School’, the ‘Generation of the 1880s’ or the ‘New Athenian School’, the ‘Generation of the 20s’, the ‘Generation of the 1930’, the ‘Post-War Generation’, the ‘Generation of the 70s’ and the 80s, and the ‘Generation of the 90s’. Romanticism and Classicism were two defining features for both the ‘Ionian Islands’ School’ and the ‘Athenian School’. Both Schools developed patriotic themes in their poetry -their poems speak about freedom, duty and fighting spirit- and focused on themes such as love and nature. Often they appear to have an idealistic conception of art. The choice of the language seems to be a distinctive element of these two Schools: the poets of the ‘Athenian School’ wrote in Katharevousa (an archaic form of Greek language), while the poets of the ‘Ionian Islands’ School’ wrote mostly in Demotic Greek and were inspired by Italian poetry (Beaton 1996, 59 -81). The ‘Generation of 1880’ or the ‘New Athenian School’ that came next was divided into three groups, the Symbolism, the Parnassianism and the Romanticism, and the poets of that period were mostly interested in establishing demotic Greek into poetry (Beaton 1996, 102-104, 120-129). The ‘Generation of the 20s’ was greatly influenced by Symbolism and can be divided into two groups of poets with slightly different directions. The poets of the first direction maintained bonds with tradition, they grieved a life which lost its ideals and used symbols in order to express their thoughts and mental conditions (for example, the poet Miltiadis Malakasis). The poets of the second direction, such as Kostas Karyotakis and Tellos Agras, also lamented their lost ideals and expressed a feeling of general fatigue and dissatisfaction. They wrote poems with a clear social message and stinging sarcasm, declaring their controversy in the society. They renewed the way of poetical expression by adding words closely to the style of prose (Beaton 1996, 168-173). The ‘Generation of the 1930’ brought an innovation into poetry as the poets aimed to renew Greek poetry verbally, thematically and metrically by introducing and establishing the free verse. The ‘Generation of the Thirties’, the avant-garde of Modern Greek Literature, avoids lyricism and sentimentality (Vitti 1995, 49) and introduces a new poetic language peculiar to spoken Greek. The themes are derived from simple everyday facts of life and common human feelings (Vitti 1995, 87-184). Subsequently, the poets of the ‘Post-war Generation’ also known as ‘Generation of Defeat’, which is subdivided into the ‘First Post-War Generation’ and

the ‘Second’, were matured during the World War II, the Nazi occupation army and the Greek Civil War (1946-1949), and their poems are marked by a sense of defeat. Particularly, the poems of the first period are depicting the experiences of those years and forming social comments (political dimension), while at the second period the social comment and the lyrical element recedes and gives rise to a more personal poetry often with tragic tone (Menti 1995). The next Generation, the ‘Generation of the 70s’, or ‘Generation of defiance/contestation’, is characterized by the broadness of the poetic voices. Despite the wide range of poets, there are some common themes deployed in their poetry, mostly related to the social, historical and political situation of their time in Greece and abroad: consumerism and intense urbanization, technological explosion, the dictatorship of the colonels in Greece (1967-1974) and the post-civil war climate of the ’50s. Their criticism and negative attitude towards any establishment or ideology, their diffuse and their nihilism often indicate an ironic and sometimes satirical language. From their first poetic impressions the poets of this generation incorporate a multiplicity of language, of textures and techniques. Their use of language it is believed to have brought a significant renewal of the poetic vocabulary, not only because poets tend to use simple words derived from the everyday speech, but also because they incorporate foreign words and their style is usually sharp and ironic (Alexiou 2001). The ‘Generation of the 80s’ or the ‘Generation of the private visions’ is a generation known for its introversion and avoidance of referring to social events or to issues of public concern. As Kefalas illustrates, the term ‘private vision’ “signifies the end of common myths and the absence of a collective vision and social collusion.” (1990, 136).

3.2 Software tools

The software used for the text analysis were two tools that generate word-frequency lists, word lists and concordances along with visualizations: AntConc and Stylo(). These two tools were chosen for my analysis because they are both free to download and available online, and they provide a multitude of options to the researcher. As

Hoover demonstrates, they can “test hunches about how specific words are used in a text [...], statistically compare word frequencies among several texts and can show which texts have unusual frequencies of words of interest [or] generate lists of collocations (words that occur repeatedly near each other).” (2013). Specifically, AntConc is an open and available software program for text analysis developed by Laurence Anthony, Professor in the Faculty of Science and Engineering at Waseda University in Japan. It is available in several versions and runs on computers running Windows, Macintosh, and Linux. It has seven tools, the Concordance, the Concordance Plot, the File View, the Clusters/n-Grams, the Collocates, the Word List and the Keyword List, each one of them has different function (Anthony 2015). I downloaded the Windows version 3.4.4.0 of AntConc. The Word List tool counts the most frequent words in the corpus and presents them in a list, an element which was helpful in my study in order to retrieve and analyze the nouns and the names. AntConc was mostly used to get word frequency lists and word lists, as it has the option to retrieve words based on their root. In that way I was able to create lemmas, something that it was important for my analysis. Stylo () is a free, available and open-source R package, written in R programming language, destined for a variety of stylometric analyses and developed by Maciej Eder, an associate professor at the Pedagogical University of Kraków, Poland, and at the Institute of Polish Language of the Polish Academy of Sciences, Jan Rybicki, Assistant Professor at the Institute of Modern Languages at the Pedagogical University of Kraków, and Mike Kestemont, Assistant Professor in the Department of Literature at the University of Antwerp4. Stylo has a variety of functions -stylo(), classify(), oppose(), rolling.delta(), rolling.classify() etc.- that can provide handy implementations of analyses for computational stylistics and can be used for a variety of purposes, such as authorship verification, stylistic analysis, genre and gender recognition etc. It is accompanied with a graphic user interface that allows the user to apply certain settings and choose from a variety of options. Moreover, it generates diverse graphs/visualizations, such as Principal Components Analysis, Cluster Analysis, Multidimensional Scaling, and Bootstrap Consensus Trees (Eder et al. 2016).

Especially, the stylo() function in the R package contains a variety of methods, such as multidimensional scaling, principal component analysis, cluster analysis, bootstrap consensus trees etc. (Eder et al. 2015, 1). More precisely, the stylo() function “processes electronic texts to create a list of all the words used in all texts studied, with their frequencies in the individual texts; selects words from the desired frequency ranges; performs additional procedures that might improve attribution, […]; compares the results for individual texts; performs a variety of multivariate analyses; presents the similarities/distances obtained in tree diagrams; and produces a bootstrap consensus tree (a new graph that combines many tree diagrams for a variety of parameter values).” (Eder et al. 2015, 4). Moreover, it gives the opportunity to the user to “automatically load and process a corpus of electronic text files from a specified folder, and to perform a variety of stylometric analyses from multivariate statistics to assess and visualize stylistic similarities between input texts.” (Eder et al. 2015, 7). Stylo was useful for my research not only for the stylistic analysis (word frequencies and word lists) but also for the production of visualization of texts based on their similarities and differences. Thus, I first installed the 64bit version of R package, i.e. the software program for statistical computing and graphics, and then the version 0.6.3 of the R package (stylo). Every time I wanted to start a R session and use stylo() I was loading the ‘stylo’ library, that simultaneously makes all functions of the R package active.

3.3 Stylistic Analysis: Style Markers and Text Collection

‘Style Markers’ or ‘indicators’ are those language features that may be part of the unique style of a writer and can be retrieved by the measurement of word frequencies, word bi-grams, word tri-grams, collocations, characters, letter bi-grams and tri-grams, syntax preferences, frequency of function words, punctuation etc. (Eder 2011). Specifically, the term ‘Word n-grams’ refers to a set of co-occurring words, letters, phonemes etc. Depending on the value of N, can occur unigrams, i.e. the individual items (words, phonemes, letters etc.), bigrams, i.e. a pair of words, trigrams, a set of three words or letters, and so forth (Wikipedia).

As ‘style markers’ I used word types and word tokens, in terms of most frequent words. Words can be calculated as types or as tokens: types are word-forms and tokens are occurrences of word-forms. As Kenny declares whenever we measure the occurrences of a word into a particular text, we measure the number of tokens of the occurrence of a particular type (1982, 66). Hoover also defines word types as unique sequences “of alphanumeric characters not broken by a space or by any punctuation except the hyphen or the apostrophe” (2010, 251). Thus, the term ‘word token’ signifies the occurrences of types, i.e. how many words appear on a text, no matter how often they are repeated; and the term ‘word type’ refers to the diverse forms of words in a text. Regarding the corpus of texts, I focus on poets of at least 3 poems in the dataset by excluding 15 poets in total (particularly 2 women and 13 men), because the number of word types and tokens of these poets is not representative. As mentioned earlier before, the total number of poems for each poet is not enough in order to have reliable and/or satisfactory results. The stylistic analysis can be effective when there is enough number of words for each poet/writer. This indeed was a significant problem in this study, since the total number of poems for each poet is small or not quite satisfactory. This happened due to the fact that the corpus of poetic texts was based on online and specific anthologies, which show a significant drawback of Modern Greek poetry that is not adequately presented on the Internet. The majority of poets on my corpus are being represented with 3 to 10 poems, however the selected set of poems constitutes a representative sample of Modern Greek Poetry: they cover two consecutive centuries, 19th and 20th, and belong to diverse poetic generations. My aim was to have as many poets as possible in order to make comparisons between the style of poets, their generations and the function of names. However, all poets have been included in the analysis of named entities. Table 1 presents all the poets of the corpus in alphabetic order (following the English alphabet). Next to the English name of each poet is given into parentheses the number of poems that I had available for analysis. I also mention the name of the poets in Greek and the year of birth and death, if applicable. The rest of the columns refer to the poetic generation that each poet can be seen to belong to, the century, and the number of word types and tokens I got for each poet.

Poet (n. of poems) Poet (in Greek) Century Classification Word Types Word Tokens TTR Aggelis, Dimitris (3) Αγγελής, Δημήτρης (1973-) 20th Generation of the 90s 347 597 58,10% Agras, Tellos (14) Άγρας, Τέλλος (1899-1944) 20th Generation of the 20s 1136 2299 49,41% Alavera, Roula (3) Αλαβέρα, Ρούλα (1943-) 20th Generation of the 70s 178 250 71,20% Alexandrou, Aris (4) Αλεξάνδρου, Άρης (1922-1978) 20th Post-War 184 296 62,16% Anagnostakis, Manolis (10) Αναγνωστάκης, Μανόλης (1925-2005) 20th Post-War 776 1487 52,18% Angelaki-Rouk, Katerina (3) Αγγελάκη-Ρουκ, Κατερίνα (1939-) 20th Post-War 269 446 60,31% Antoniou, Dimitrios I. (16) Αντωνίου, Δημήτριος I. (1906-1994) 20th Generation of the 30s 700 1410 49,64% Aslanoglou, Nikos-Alexis (28) Ασλάνογλου, Νίκος-Αλέξης (1931-1996) 20th Post-War 967 2114 45,74% Cavafy, K. P. (259) Καβάφης, K.Π. (1863-1933) 20th No generation 9570 31676 30,21% Chatzopoulos, Konstantinos (9)Χατζόπουλος, Κωνσταντίνος (1868-1920) 19th Generation of 1880 399 866 46,07% Chatzopoulos, Thanasis (10) Χατζόπουλος, Θανάσης (1961-) 20th Generation of the 80s 382 691 55,28% Chionis, Argyris (3) Χιόνης, Αργύρης (1943-2011) 20th Generation of the 70s 109 171 63,74% Chouliarakis, Dimitris (3) Χουλιαράκης, Δημήτρης (1957- ) 20th Generation of the 80s 299 451 66,29% Christianopoulos, Ntinos (5) Χριστιανόπουλος, Nτίνος (1931-) 20th Post-War 515 939 54,84% Christodolou, Dimitra (3) Χριστοδούλου, Δήμητρα (1953-) 20th Generation of the 70s 269 444 60,58% Chronas, Giorgos (3) Χρονάς, Γιώργος (1948- ) 20th Generation of the 70s 337 530 63,58% Dallas, Yiannis (11) Δάλλας, Γιάννης (1924-) 20th Post-War 393 694 56,62% Daskalopoulos, Dimitris (3) Δασκαλόπουλος, Δημήτρης (1939-) 20th Post-War 300 434 69,12% Davvetas, Nikos (3) Δαββέτας, Νίκος Γ. (1960-) 20th Generation of the 80s 261 433 60,27% Dimakis, Minas (10) Δημάκης, Mηνάς (1914-1980) 20th Post-War 734 1412 51,98% Dimoula, Kiki (19) Δημουλά, Kική (1931-) 20th Post-War 1400 2897 48,32% Dimoulas, Athos (4) Δημουλάς, Άθως (1921-1985) 20th Post-War 181 283 63,95% Doukaris, Dimitris (7) Δούκαρης, Δημήτρης (1925-1982) 20th Post-War 365 680 53,67% Drosinis, Giorgos (4) Δροσίνης, Γεώργιος (1859-1951) 19th Generation of 1880 156 219 71,23% Efstathiadis, Yiannis (3) Ευσταθιάδης, Γιάννης (1946- ) 20th Generation of the 70s 164 241 68,04% Eleftherakis, Dimitris (4) Ελευθεράκης, Δημήτρης (1978-) 20th Generation of the 90s 277 459 60,34% Eleftheriou, Manos (5) Ελευθερίου, Mάνος (1938-) 20th Post-War 305 539 56,50% Elytis, Odysseas (22) Ελύτης, Οδυσσέας (1911-1996) 20th Generation of the 30s 2289 5667 40,39% Embirikos, Andreas (25) Εμπειρίκος, Ανδρέας (1901-1975) 20th Generation of the 30s 1805 4101 44,00% Emmanouil, Kaisar (6) Εμμανουήλ, Καίσαρ (1902-1970) 20th Generation of the 20s 553 936 59,08% Engonopoulos, Nikos (25) Εγγονόπουλος, Νίκος (1907-1985) 20th Generation of the 30s 2303 5164 44,59% Filyras, Romos (12) Φιλύρας, Pώμος (1888-1942) 20th Generation of the 20s 617 1044 59,09% Fokas, Nikos (6) Φωκάς, Νίκος (1927-) 20th Post-War 378 604 62,58% Fostieris, Antonis (3) Φωστιέρης, Αντώνης (1953-) 20th Generation of the 70s 367 570 64,38%

Frantzi, Anteia (3) Φραντζή, Άντεια (1945-) 20th Generation of the 70s 165 246 67,07% Georgousis, Giorgos (3) Γεωργούσης, Γιώργος (1941-) 20th Post-War 252 355 70,98% Geralis, Giorgos (6) Γεραλής, Γιώργος (1917-1996) 20th Post-War 428 804 53,23% Gkanas, Michalis (7) Γκανάς, Μιχάλης (1944-) 20th Generation of the 70s 606 1206 50,24% Gkatsos, Nikos (3) Γκάτσος, Νίκος (1911-1992) 20th Generation of the 30s 1154 2440 47,29% Gkorpas, Thomas (4) Γκόρπας, Θωμάς (1935-2003) 20th Post-War 268 501 53,49% Gotis, Giorgos (3) Γώτης, Γιώργος (1956-) 20th Generation of the 80s 283 445 63,59% Gryparis, Ioannis N. (9) Γρυπάρης, Ιωάννης (1870-1942) 19th Generation of 1880 750 1499 50,03% Kaknavatos, Ektor (4) Κακναβάτος, Έκτωρ (1920-2010) 20th Post-War 271 456 59,42% Kalogeropoulos, Angelos (3) Καλογερόπουλος, Άγγελος (1959- ) 20th Generation of the 80s 155 201 77,11% Kalvos, Andreas (21) Κάλβος, Aνδρέας (1792-1869) 19th Ionian Islands' School 3296 8201 40,19% Karasoutsas, Ioannis D. (5) Καρασούτσας, Ιωάννης (1824-1873) 19th Athenian School 801 1317 60,82% Karavasilis, Giorgos (3) Καραβασίλης, Γιώργος (1949-2000) 20th Generation of the 70s 245 410 59,75% Karelli, Zoe (11) Καρέλλη, Ζωή (1903-1998) 20th Generation of the 30s 889 1725 51,53% Karouzos, Nikos (9) Καρούζος, Nίκος (1926-1990) 20th Post-War 432 726 59,50% Karyotakis, K.G. (24) Καρυωτάκης, K.Γ. (1896-1928) 20th Generation of the 20s 1296 2537 51,08% Kassos, Vangelis (3) Κάσσος, Βαγγέλης (1956- ) 20th Generation of the 80s 247 414 59,66% Katsaros, Michalis (4) Κατσαρός, Μιχάλης (1919-1998) 20th Post-War 391 737 53,05% Kavvadias, Nikos (8) Καββαδίας, Nίκος (1910-1975) 20th Generation of the 30s 895 1689 52,98% Kefalas, Elias (3) Κεφάλας, Ηλίας (1951- ) 20th Generation of the 70s 269 425 63,29% Kontos, Yiannis (3) Κοντός, Γιάννης (1943-2015) 20th Generation of the 70s 163 223 73,09% Kotzioulas, Giorgos (4) Κοτζιούλας, Γιώργος (1909-1956) 20th Generation of the 20s 300 458 65,50% Koutsourelis, Kostas (3) Κουτσουρέλης, Κώστας (1967-) 20th Generation of the 90s 73 86 84,88% Kozias, Giorgos (3) Κοζίας, Γιώργος (1958-) 20th Generation of the 80s 139 197 70,55% Krystallis, Kostantinos D. (3) Κρυστάλλης, Κώστας (1868-1894) 19th Generation of 1880 340 703 48,36% Kyparissis, Panos (3) Κυπαρίσσης, Πάνος (1945-) 20th Generation of the 70s 226 333 67,86% Kyrou, Kleitos (4) Κύρου, Κλείτος (1921-2006) 20th Post-War 311 477 65,19% Kyrtzaki, Maria (3) Κυρτζάκη, Μαρία (1948-2016) 20th Generation of the 70s 376 705 53,33% Lagios, Elias (3) Λάγιος, Ηλίας Α. (1958-2005) 20th Generation of the 80s 346 528 65,53% Laina, Maria (4) Λαϊνά, Μαρία (1947-) 20th Generation of the 70s 124 166 74,69% Lantavos, Kostas (3) Λάνταβος, Kώστας (1949-) 20th Generation of the 70s 175 238 73,52% Lapathiotis, Napoleon (4) Λαπαθιώτης, Ναπολέων (1888-1944) 20th Generation of the 20s 234 439 53,30% Laskos, Orestis (3) Λάσκος, Ορέστης (1908-1992) 20th Generation of the 30s 351 619 56,70% Lazaris, Nikos (3) Λάζαρης, Νίκος (1947-) 20th Generation of the 70s 229 327 70% Leivaditis, Tasos (14) Λειβαδίτης, Tάσος (1921-1988) 20th Post-War 879 1738 50,57% Leontaris, Vyron (5) Λεοντάρης, Bύρων (1932-2014) 20th Post-War 333 701 47,50% Liontakis, Christoforos (3) Λιοντάκης, Χριστόφορος (1945- ) 20th Generation of the 70s 248 383 64,75% Lykiardopoulos, Gerasimos (4) Λυκιαρδόπουλος, Γεράσιμος (1936-) 20th Post-War 239 410 58,29% Malakasis, Miltiadis (10) Μαλακάσης, Mιλτιάδης (1870-1943) 19th Generation of 1880 559 1031 54,21% Markopoulos, Giorgos (3) Μαρκόπουλος, Γιώργος (1951- ) 20th Generation of the 70s 347 569 60,98% Martzokis, Stefanos (3) Μαρτζώκης, Στέφανος (1855-1913) 19th Ionian Islands' School 197 330 59,69% Mastoraki, Jenny (5) Μαστοράκη, Tζένη (1949-) 20th Generation of the 70s 361 575 62,78% Matsas, Alexandros (4) Μάτσας, Αλέξανδρος (1910-1969) 20th Generation of the 30s 760 1297 58,59% Mavilis, Lorentzos (7) Μαβίλης, Λορέντζος (1860-1912) 19th Ionian Islands' School 392 581 67,46% Mavroudis, Kostas (3) Μαυρουδής, Κώστας (1948- ) 20th Generation of the 70s 481 803 59,90% Melissanthi (5) Μελισσάνθη (1909-1990) 20th Generation of the 30s 259 560 46,25% Meskos, Markos (3) Μέσκος, Mάρκος (1935-) 20th Post-War 305 477 63,94% Mpravos, Christos (8) Μπράβος, Xρήστος (1948-1987) 20th Generation of the 70s 315 516 61,04% Niarchos, Thanasis (5) Νιάρχος, Θανάσης Θ. (1945-) 20th Generation of the 70s 278 484 57,43% Oikonomou, Zisis (4) Οικονόμου, Zήσης (1911-2005) 20th Generation of the 30s 315 542 58,11% Ouranis, Kostas (4) Ουράνης, Κώστας (1890-1953) 20th Generation of the 20s 231 373 61,93% Palamas, Kostis (14) Παλαμάς, Κωστής (1859-1943) 19th Generation of 1880 3131 9014 34,73% Panayiotopoulos, I.M. (3) Παναγιωτόπουλος, I.M. (1901-1982) 20th Generation of the 20s 288 612 47,05% Papaditsas, D. P. (4) Παπαδίτσας, Δ.Π. (1922-1987) 20th Post-War 530 951 55,73% Papageorgiou, Kostas G. (3) Παπαγεωργίου, Κώστας Γ. (1945- ) 20th Generation of the 70s 205 388 52,83% Papantoniou, Zacharias (3) Παπαντωνίου, Ζαχαρίας (1877-1940) 19th Generation of 1880 268 375 71,46% Paparrigopoulos, Dimitrios (18)Παπαρρηγόπουλος, Δημήτριος (1843-1873) 19th Athenian School 1980 4430 44,69% Papathanasopoulos, Thanasis (3)Παπαθανασόπουλος, Θανάσης (1937-) 20th Post-War 265 395 67,08% Pappa, Lena (3) Παππά, Λένα (1932-) 20th Post-War 230 391 58,82% Paraschos, Achilleas (8) Παράσχος, Αχιλλέας (1838-1895) 19th Athenian School 1289 2722 47,35% Paschalis, Stratis (3) Πασχάλης, Στρατής (1958-) 20th Generation of the 80s 267 398 67,08% Patilis, Yannis (3) Πατίλης, Γιάννης (1947-) 20th Generation of the 70s 148 204 72,54% Patrikios, Titos (11) Πατρίκιος, Tίτος (1928-) 20th Post-War 462 811 56,96% Pavlopoulos, George (3) Παυλόπουλος, Γιώργης (1924-2008) 20th Post-War 265 449 59,02%

Pentzikis, Nikos Gabriel (3) Πεντζίκης, Νίκος Γαβριήλ (1908-1993) 20th Generation of the 30s 514 801 64,16% Polydouri, Maria (6) Πολυδούρη, Mαρία (1902-1930) 20th Generation of the 20s 331 640 51,71% Porfyras, Lampros (11) Πορφύρας, Λάμπρος (1879-1932) 19th Generation of 1880 647 1507 42,93% Poulios, Leuteris (3) Πούλιος, Λευτέρης (1944-) 20th Generation of the 70s 330 531 62,14% Pratikakis, Manolis (3) Πρατικάκης, Mανόλης (1943-) 20th Generation of the 70s 324 549 59,01% Psarras, Charis (3) Ψαρράς, Χάρης (1982-) 20th Generation of the 90s 155 191 81,15% Rantos, Nikitas (3) Ράντος, Νικήτας (1907-1988) 20th Generation of the 30s 193 249 77,51% Ritsos, Yiannis (18) Ρίτσος, Γιάννης (1909-1990) 20th Generation of the 30s 1925 4794 40,15% Sachtouris, Miltos (7) Σαχτούρης, Mίλτος (1919-2005) 20th Post-War 404 735 54,96% Sarantaris, Giorgos (19) Σαραντάρης, Γιώργος (1908-1941) 20th Generation of the 30s 576 1167 49,35% Seferis, George (19) Σεφέρης, Γιώργος (1900-1971) 20th Generation of the 30s 1449 3083 46,99% Sikelianos, Angelos (17) Σικελιανός, Άγγελος (1884-1951) 20th No generation 2604 7043 36,97% Sinopoulos, Takis (5) Σινόπουλος, Tάκης (1917-1981) 20th Post-War 961 2052 46,83% Siotis, Ntinos (3) Σιώτης, Ντίνος (1944- ) 20th Generation of the 70s 199 302 65,89% Skarimpas, Yiannis (4) Σκαρίμπας, Γιάννης (1893-1984) 20th Generation of the 30s 342 591 57,86% Solomos, Dionysios (17) Σολωμός, Διονύσιος (1798-1859) 19th Ionian Islands' School 4249 14319 29,67% Stergiopoulos, Kostas (3) Στεργιόπουλος, Kώστας (1926-2016) 20th Post-War 308 525 58,66% Steriadis, Vasilis (3) Στεριάδης, Bασίλης (1947-2003) 20th Generation of the 70s 315 534 58,98% Tasulis, Themis (3) Τασούλης, Θέμης (1936-2008) 20th Post-War 197 322 61,18% Tertsetis, Georgios (9) Τερτσέτης, Γιώργος (1800-1880) 19th Ionian Islands' School 942 1804 52,21% Themelis, Giorgos (3) Θέμελης, Γιώργος (1900-1976) 20th Post-War 351 636 55,18% Theocharis, Giorgos (3) Θεοχάρης, Γιώργος (1951-) 20th Generation of the 80s 177 293 60,40% Theotokis, Konstantinos (4) Θεοτόκης, Κωνσταντίνος (1872-1923) 19th Generation of 1880 249 385 64,67% Traianos, Alexis (3) Τραϊανός, Αλεξης (1944-1980) 20th Generation of the 70s 319 502 63,54% Tsiropoulos, Kostas E. (3) Τσιρόπουλος, Κώστας Ε. (1930-) 20th Post-War 217 311 69,77% Vakalo, Eleni (3) Βακαλό, Ελένη (1921-2001) 20th Post-War 269 458 58,73% Valaoritis, Aristotelis (17) Βαλαωρίτης, Αριστοτέλης (1824-1879) 19th Ionian Islands' School 3251 8498 38,25% Valaoritis, Nanos (3) Βαλαωρίτης, Νάνος (1921-) 20th Post-War 301 485 62,06% Varnalis, Kostas (7) Βάρναλης, Κώστας (1884-1974) 20th No generation 736 1322 55,67% Varveris, Yiannis (3) Βαρβέρης, Γιάννης (1955-2011) 20th Generation of the 70s 226 326 69,32% Varvitsiotis, Takis (4) Βαρβιτσιώτης, Τάκης (1916-2011) 20th Post-War 228 387 58,91% Vayenas, Nasos (3) Βαγενάς, Νάσος (1945-) 20th Generation of the 70s 231 361 63,98% Veis, George (3) Βέης, Γιώργος (1955-) 20th Generation of the 70s 271 420 64,52% Vizyenos G.M. (7) Βιζυηνός, Γ.Μ. (1849-1896) 19th Generation of 1880 1084 2159 50,20% Vlavianos, Charis (4) Βλαβιανός, Χάρης (1957-) 20th Generation of the 80s 332 531 62,52% Votsi, Olga (3) Βότση, Όλγα (1922-1998) 20th Post-War 186 295 63,05% Vrettakos, Nikiforos (5) Βρεττάκος, Νικηφόρος (1912-1991) 20th Generation of the 30s 310 569 54,48% Xanthopoulos, Lefteris (3) Ξανθόπουλος, Λευτέρης (1945-) 20th Generation of the 70s 231 388 59,53% Yfantis, Yannis (3) Υφαντής, Γιάννης (1949-) 20th Generation of the 70s 159 296 53,71% Zafeiriou, Stavros (3) Ζαφειρίου, Σταύρος (1958-) 20th Generation of the 80s 245 410 59,75%

Total Number 87930 196158 Table 1. Modern Greek Poetry dataset

4. Findings

The first part of this study aims at finding any correlations or differences regarding the use of the nouns between the poets from different generations and/or gender: it deals with the Modern Greek Poetry of the 19th and the 20th century and with texts from several time periods. Due to lack of time, I mostly focused on the analysis of the nouns and less on the verbs, adjectives and adverbs. I did not take into consideration the function words. The first step was to analyze the differences between poets by looking at the word frequencies (which words are being used more frequent) and the second one the usage of names, their frequencies and function. To what extent poets’ writing styles are much the same or distinguishable? In accomplishing this, I used Stylo and AntConc, in order to track and highlight the differences and similarities between poets and generations. In order to study and analyze the correlations between Modern Greek poets it was necessary to categorize them by gender, century and poetic generation. Using the two above mentioned software tools I acquired 38,982 word types and 196,158 word tokens in total (type-token ratio 19.87%).

Table 2: The Excel file with the 10 most frequently occurring Words (MFW)

Based on the results I got from AntConc, I saved the outcome (wordlist) presenting the frequency of the words firstly for the whole corpus of texts (Modern Greek Poetry) and secondly for each poetic generation. I repeated the same procedure later on for female and male poets by creating two word lists respectively (see chapter: ‘Results per Gender’). In order to have a more manageable file I changed the format of the file, from a ‘txt file’ to an ‘excel file’. Table 2 depicts part of the ‘excel file’ containing three columns: the rank, the frequency of the word and the word token (an English translation of the Greek word is being given at the fourth column). Regarding the third list of words -the one that contains the Named entities- I followed the same procedure, although I added two more columns, referring to the poet who makes use of the Name and to the category of the Name (see: Chapter 5). The following chapter will focus on the results I got on the whole corpus, by poetic generation and by gender.

4.1 Most Frequent Words (MFW)

4.1.1 Results on the whole corpus of texts

In the ‘excel file’ of ‘Most Frequent Words’ I added two more columns, the ‘Type’ and the ‘Category’ column. The ‘Type’ column refers to the type of words -function and/or content words- and the ‘Category’ column denotes the subdivision of words according to their grammatical type (conjunction word, article, particle etc.). The forms of Greek articles and some prepositions vary as they take on the case of the declension of the following noun. In that case I put into brackets the gender of each article (m.=male, f.=female, n.=neutral) and the singular or plural form of the article. As can be seen in table 3, the majority of the 60 most frequent words of the whole corpus are function words (i.e. conjunctions, articles, particles, pro-sentences, prepositions, pronouns etc.). Especially, the articles, the conjunction words and the prepositions dominate amongst the function words, while some of them belong to two types of words simultaneously (both content and function words, as can be seen in the case of the word ‘where’). The only verb that appears in the list is a form of the verb ‘to be’ in the third singular person and in the present tense (is). Subsequently, the two nouns that appear more frequently are the words eyes and light, in singular and plural form respectively. It is interesting to take into consideration the abbreviated forms of some Greek function words, such as the conjunction word and that has two abbreviations (“κι” and “κ”). In case of adding the appearance of these abbreviations to the word and, it makes a total number of 10,172 occurrences. The same can be followed for the words that appear with apostrophe after the elision/omission of the vowel of the word: for example the word from appears with two separate forms in the text: από and απ’ (an apostrophe is used when the first letter of the next word is a vowel). It is worth mentioning that amongst the 60 first most used words, we do not encounter enough adjectives and/or adverbs compared to nouns and verbs.

Rank Frequency Word in Greek Word in English Type of Word Category 1 6804 και and Function Conjunction 2 5067 το the [n. singular] Function Article 3 3777 τα the [n. plural] Function Article 4 3774 του of/his [m. singular] Function Article 5 3421 να to Function Particle, Conjunction 6 2991 που where Content, Function Adverb,Conjunction 7 2845 η the [f. singular] Function Article 8 2461 με with Function Preposition 9 2446 την the [f. singular] Function Article 10 2329 της of/her [f. singular] Function Article 11 2294 ο the [m. singular] Function Article 12 2277 κι and Function Conjunction 13 1957 μου my Content Possesive adjective 14 1730 τον the [m.singular] Function Article 15 1488 δεν not Function Particle 16 1390 τους their/them [m.plural] Function Article 17 1313 από from Function Preposition 18 1311 στο at Function Preposition 19 1274 τη the [f. singular] Function Article 20 1213 σου your Content Possesive adjective 21 1203 οι the [m. plural] Function Article 22 1169 θα will Function Particle 23 1136 των of Function Article 24 1083 για for Function Preposition 25 1053 σε at Function Preposition 26 1009 στα to Function Conjunction 27 928 σαν as Content Adverb 28 921 στην to Function Conjunction 29 912 μας us/our Function Pronoun/Adjective 30 907 απ' from Function Preposition 31 896 τ' the Function Article 32 828 είναι is Content Verb 33 748 μια a/an Function Article 34 740 ένα one,a/an Function Article/ 35 707 κ' and Function Conjunction 36 587 στη to Function Conjunction 37 567 στον to/at Function Conjunction 38 544 τις [f. plural] the Function Article 39 528 σ' at Function Preposition 40 527 εις in, to, at Function Conjunction

41 527 μ' with Function Preposition 42 476 μέσα in Function Preposition 43 459 μες in Function Preposition 44 438 τι what Function Pronoun 45 431 όταν when Function Conjunction 46 423 αν if Function Conjunction 47 405 πως that Function Conjunction 48 394 ως as Function Preposition 49 371 τώρα now Content Adverb 50 355 ν' to Function Particle, Conjunction 51 350 δε not Function Particle 52 335 μάτια eyes Content Noun 53 302 εκεί there Content Adverb 54 301 ή or Function Conjunction 55 287 σα as Function Preposition 56 281 φως light Content Noun 57 279 πάντα always Content Adverb 58 276 μα but Function Conjunction 59 273 όλα all Content Adjective 60 263 πιο more Content Adverb

Table 3: The 60 Most Frequent Words in Modern Greek Poetry as they appear to the ‘excel file’, for which an English translation is being given

Looking closely to all the results, the function words and the nouns appear the most compared to the adjectives and the adverbs. The most frequent verbs that appear on the whole corpus are types of auxiliary or main verbs, such as forms of the verbs “be”, “have”, “can” and “see”, in various tenses and grammatical persons. Especially, regarding the content words -i.e. nouns, adjectives, adverbs, and verbs- they tend to appear with different suffixes, due to Greek language’s grammatical cases and declensions. More specific, the nouns appear in plural and singular forms and other diverse forms due to declensions. Table 4 presents the ten most frequent nouns according to the ‘Most Frequent Word’ list I got from the AntConc, in which I did not take into consideration other forms of the same word that might appear on the list (for example the plural form of a specific word). The ‘Rank’ column refers to each word’s position on the whole list and the ‘Frequency’ column to the number of appearance.

Table 4: The ten most frequent nouns in Modern Greek Poetry

However, in case of counting all the different forms of each noun (declensions, forms, suffixes), the results might be different than the ones mentioned on table 4. In order to see whether this happens or not, I counted all the different forms of the nouns by using the ‘Wildcards’ in AntConc. ‘Wildcards’ is a way to broaden the search of one word or word form on AntConc by typing certain characters instead of letters. In that way I was able to find all occurrences of the words I was looking for. I wrote down the diverse forms of the noun on an ‘excel file’ that appear on the list and unified them in one lemma. Table 5 presents the ten most frequent nouns regarding their different form appearances on texts.

Table 5: The ten most frequent nouns in Modern Greek Poetry taking into consideration any different forms

The results are quite the same as in table 4: the same nouns appear again only in a different position. What is interesting though in table 5 are the different forms of each noun, as they depict the development of Greek poetic language: poets of the 19th century use different forms of the same word compared to poets of the 20th century (for example, “ομμάτια”, “χείρες” and “μάτια”, “χέρια”). Overall, the analysis of nouns declares features of Modern Greek Poetry, such as themes related to nature (earth, sea) and time (life, hour), and the development of Greek poetic language that passed from Katharevousa to Demotic Greek and to more simple forms of words (linguistic perspective). Moreover, results reveal a tense of Modern Greek Poetry to be richer in nouns rather than adjectives, and to use basic and/or auxiliary verbs.

4.1.2 Word-Frequency Results by Generations

In this chapter I am trying to compare poetic generations by pointing out any differences or similarities in style between them. I order to do so, I used both AntConc and Stylo(), after putting in one ‘txt file’ all the poems divided by the poetic generations in which they belong. In order to unify the ‘txt files’ that contain the poems, I downloaded the version 2.0.2 of ‘TXT Collector’, a free text files merging tool created by David De Groot, and uploaded the unified files on the two tools. Table 6 depicts the total amount of word types and tokens per generation as given by the two tools, AntConc and Stylo(). It is worth mentioning that the difference between the files is large and the total amount of word tokens differs greatly per generation: although the ‘Generation of the 70s’ has almost the same amount of words as the ‘Generation of 1880’, the total amount of tokens differs greatly. Therefore, I calculated the type/token-ratio (TTR), i.e. the ratio of the total number of different words that are being used to the number of total words. This calculation can show the “richness” of a text, in terms of its vocabulary forms/variety of words being used. The type/token ratio is being given as a percentage that can be calculated by the division of the number of word types to the number of word tokens. In case of a high type/token ratio, which inclines 100%, the words in texts are diverse and fewer words are being repeated over and over again. On the contrary, a low type/token ratio, that inclines 0%, signifies the repetition of specific words (Moreton 2013, 105).

Poetic Generation Word types Word Tokens Type/Token ratio (RRT) Ionian Islands’ School 9,873 33,733 29.26% Athenian School 3,527 8,469 41.64% Generation of 1880 5,690 17,758 32.04% Generation of the 20s 3,255 7,990 40.73% Generation of the 30s 10,848 36,467 29.74% Post-War Generation 8,560 28,459 30.07% Generation of the 70s 5,186 14,616 35.48% Generation of the 80s 2,204 4,992 44.15% Generation of the 90s 729 1,333 54.68%

Table 6: Word types; Word tokens and Type/Token ratios per poetic generation

Table 6 shows that the ‘Ionian Islands School’ appears to have the lowest type/token ratio (29.26%) compared to the other generations, followed by the ‘Generation of the 30s’ (29.74%) and the ‘Post-War Generation’ (30.07%). On the contrary, the highest type/token ratio belongs to the ‘Generation of the 90s’ (54.68%) and the ‘Generation of the 80s’ (44.15%). According to those percentages, it seems that the poets of the ‘Ionian Islands School’ tend to repeat same words, whereas the poets of the ‘Generation of the 90s’ appear to use diverse words and therefore their vocabulary is richer. This difference though possibly reflects the difference between the files, as mentioned before. As Moreton argues “the larger the corpus the more likely some words, particularly grammar words, are repeated, which in turn will reduce the type/token ratio.” (2013, 105). Given the fact that the three most frequent nouns in the whole corpus (see Table 4) are eyes, light and night, I wanted to look the relative frequency of those words per generation. Specifically, I wanted to see in which group of texts each one of these nouns appears the most. Therefore, I uploaded the .txt files with the poems divided by poetic generations and first used the ‘Concordance Plot’ tab in AntConc. This tab visualizes the appearance of a chosen word throughout the file, and presents

it as a “barcode” per file. The ‘Generation of the 30s’ uses the noun eyes more times compared to the other generations (see Table 7). More specific, the word eyes has 81 occurrences in poets of the ‘Generation of the 30s’, while it appears with 64 hits in the file of the ‘Post-War Generation’ poetry. Taking into consideration the difference between those two files (the file length counted in characters appears diverse between them), it could be said that it might signify a relation between those two poetic generations. I will examine this possibility later on.

Table 7: Occurrence of the noun eyes by using the ‘Concordance plot’ tool on AntConc

I repeated the same procedure in order to find the relative frequency of the 10 most frequent nouns for all the Generations. Table 8 presents the frequency of each noun per poetic generation. As can be seen, the ‘Generation of the 30s’, the ‘Post-War Generation’ and the ‘Ionian Islands School’ are the three generations that use nouns

the most, while the ‘Generation of the 90s’ comes last in the list. This is might related to the small number of poetic texts that the file consists of.

Poetic Generation NOUNS eyes light night life hands soul heart hour/time earth sea Total Ionian Islands’ School 55 36 27 26 38 42 28 30 63 39 384 Athenian School 3 4 2 8 3 16 5 6 13 0 60 Generation of 1880 21 35 24 23 20 28 19 18 23 10 221 Generation of the 20s 19 13 17 14 11 13 14 8 3 4 116 Generation of the 30s 81 59 49 40 40 26 41 31 21 43 431 Post-War Generation 64 67 48 54 43 15 37 25 19 45 417 Generation of the 70s 24 18 20 10 13 10 4 13 6 7 125 Generation of the 80s 9 12 11 12 6 1 2 3 2 3 61 Generation of the 90s 2 5 8 0 1 0 1 2 1 0 20

Table 8: Occurrences of the ten most frequent nouns presented per poetic generation

Poetic Generation NOUNS sword glory death sky nations mountain rock sun moon blood voice years Ionian Islands’ School 15 17 7 14 12 4 18 23 9 40 40 3 Athenian School 1 1 11 2 0 1 1 8 1 6 8 0 Generation of 1880 0 4 2 4 1 2 2 8 10 3 9 5 Generation of the 20s 0 0 7 2 0 1 2 5 4 3 3 9 Generation of the 30s 3 13 11 21 1 5 15 29 15 23 21 29 Post-War Generation 0 4 20 4 0 6 6 17 11 28 27 37 Generation of the 70s 1 1 10 1 1 5 3 9 3 16 19 19 Generation of the 80s 0 0 7 3 0 1 2 2 2 3 3 4 Generation of the 90s 0 0 2 1 0 0 1 2 3 3 1 1 n. of hits 20 40 77 52 15 25 50 103 58 125 131 107

day house body love joy lips children fire hair water world leaves Ionian Islands’ School 17 6 11 12 23 19 18 17 13 28 24 19 Athenian School 0 0 6 1 3 6 1 6 2 0 5 3 Generation of 1880 14 5 3 14 22 8 6 15 8 10 13 8 Generation of the 20s 3 2 2 5 7 3 4 1 7 4 2 6 Generation of the 30s 33 27 20 22 16 13 28 23 25 16 13 20 Post-War Generation 21 27 24 22 4 18 19 10 15 13 14 15 Generation of the 70s 20 0 21 9 2 3 12 9 8 10 11 7 Generation of the 80s 2 4 4 2 1 1 4 6 3 4 1 2 Generation of the 90s 0 1 1 2 1 2 1 0 1 0 1 2 n. of hits 110 96 92 89 79 73 93 87 82 85 84 82

Table 9: A sample of the most frequent nouns presented per poetic generation

By using the tool “Collocates” of AntConc, I wanted to see which words occur before or after the noun eyes more frequent. The ‘Window Span’ was adjusted to two words left and two words right. The majority of the words that appear on the left, belong to function words or possessive adjectives, while for the opposite side I have enough adjectives.

Rank Frequency Range Term position on left English 1 34 5 μάτια της her eyes 2 32 7 μάτια σου your eyes 3 22 6 μάτια μου my eyes 4 21 5 μάτια του his eyes 5 16 5 μάτια τους their eyes 6 11 5 μάτια και eyes and 7 11 4 μάτια των eyes of 8 5 4 μάτια που eyes that 9 4 4 μάτια να eyes to 10 4 3 μάτια τα eyes the

Rank Frequency Range Term position on right English 1 136 8 τα μάτια the eyes 2 68 8 στα μάτια in the eyes 3 7 4 με μάτια with 4 3 2 δεμένα μάτια blindfold 5 3 3 και μάτια and eyes 6 3 1 μεγάλα μάτια big eyes 7 3 2 σου μάτια your eyes 8 3 3 ωραία μάτια beautiful eyes 9 2 1 άγρια μάτια wild eyes 10 2 2 δυο μάτια two eyes

Table 10: Collocates of the noun eyes, sorted by frequency

Table 11: Collocates. Occurrences of words that appear before or after the word eyes, sorted by frequency

As my main aim is to study the frequency of nouns in Modern Greek poetry, I repeated the same procedure for each poetic generation in order to find which nouns are being used the most by poetic generation (see Table 9). For that I first looked at the first 100 nouns that appear the most and some theme related words (such as sword that might refer to war, fight, battle etc.), by looking at the word list I got from AntConc, and then used the ‘Concordance Plot’ tool in order to see which one of them appears more frequent in which poetic generation. Regarding the most frequent nouns that appear in the ‘Ionian Islands School’, they are related to national battles and fights and usually express national ideas related to freedom and independence. It is true that poets of that time are influenced by the battles of Greece for its independence against Turkey. Thus, I encountered theme related words that some of them they do not appear to other generations (mainly generations of the 20th century) or appear less, such as σπαθί/sword, δόξα/glory, θάνατος/death, ουρανός/sky and nations/έθνη, while I also found words referring to nature, like mountain/βουνό, rock/πέτρα, sun/ήλιος, and moon/φεγγάρι. Subsequently, amongst the most frequent nouns of the ‘Generation of 1880’ I encountered the nouns: joy/χαρά, fire/φωτιά, and μαλλιά/hair (see Table 9), whereas words such as roses/ρόδα, αυγή/dawn, country/χώρα, αστέρια/stars, and references to various parts of the human body: στόμα/mouth, σάρκα/flesh, lips/χείλη.

Regarding the generations of the 20th century Modern Greek poetry, the ‘Generation of the 20s’ use words, like birds/πουλιά, destiny/μοίρα, face/πρόσωπο, spring/άνοιξη, sorrow/θλίψη, while the ‘Generation of the 30s’ uses nouns such as years/χρόνια, day/μέρα, moment/στιγμή, hair/μαλλιά, road/δρόμος, bread/ψωμί, snow/χιόνι etc.. The ‘Post-war Generation’s’ most frequent words include nouns such as night/νύχτα, years/χρόνια, house/σπίτι, voice/ φωνή, body/σώμα, words/λόγια, rain/βροχή, world/κόσμος, windows/παράθυρα, words/λέξεις, memory/μνήμη, music/μουσική, songs/τραγούδια, poetry/ποίηση and poems/ποιήματα, crowd/πλήθος, summer/καλοκαίρι. It seems that this generation is mostly concerned about the poetry as art, and the effort of the poet to feel part of the crowd. The ‘Generation of the 70s’ use nouns such as voice/ φωνή, day/ημέρα, book/βιβλίο, morning/πρωί, fear/φόβος, door/πόρτα, body/σώμα, and end/τέλος. The poets tend to speak mostly about their feelings and thoughts and express their fears as the time passes by. Subsequently, the ‘Generation of the 80s’ uses nouns such as death/θάνατος and fire/φωτιά, as well as darkness/σκοτάδι, people/άνθρωποι, fear/φόβος etc. The generation encounters the life with a nihilistic view, sometimes pessimistic and hopeless. It is worth mentioning that poets use English and French words or Latin phrases, like memento mori, and that happens gradually through the generations. The latest generations tend to use more English words than the previous generations, depicting in a way the gradual opening of Greek into other languages: the style of the latest generations is akin/close to the simple everyday speech and thus it avoids elaborate words. The next step was to visualize the comparison between the poetic generations by using Stylo(). Regarding the MFW settings, I first put the minimum of words to the value 10 and the maximum to 100 and then I increased only the maximum value to 200 (n-gram size: 1). Figure 5 depicts the relation between the generations of the 20th and the 19th century respectively, as well as the correlations between the ‘Generation of the 30s’ and the ‘Post- War Generation’ (see also the related results by AntConc).

Figure 5:. The Consensus tree that depicts the comparison between poetic generations

4.1.2.1 Conclusion

By focusing on the meaning of the most frequent nouns and briefly analyzing them, I was able to see the gradual development of themes in Modern Greek poetry: from the fights of the Greek nation against the enemy (as depicted in the 19th century Greek poetry), seen into words like sword, enemy etc. to the expression of feelings and personal emotions, like loneliness and fear (20th century poetry). Furthermore, I was able to understand the gradual anguish of the 20th century human, as a person that worries about the passage of time (through the frequent use of the words time and years). Overall, the study of the most frequent words per generations and centuries gave us a great view of the transition of Modern Greek poetry from collectivity (we) to individuality (I) and helped me specify the relations between poetic generations, especially between the ‘Generation of the 30s” and the ‘Post- War Generation’. In conclusion, I found that Modern Greek poetry is more focused on nouns related to nature and to parts of human body as a way to express feelings and thoughts, rather than adjectives.

4.1.3 Results by Gender

Having examined the nouns and their frequencies for each poetic generation, the next step was to study the differences in most frequent words comparing female and male poets, regardless of the generation in which they are classified. The corpus consists of 16 female poets of the 20th century, as no female poet of the 19th century has been included in the two online anthologies, and 111 male poets of the 20th century. Male poetry is being represented by a vast and diverse group of poets, classified in different generations, from the 20th and the 19th century. In order to have an equivalent comparison (as much as is possible, due to the fact that there are already more male than female poets in the corpus), I excluded for my analysis any male poets that belong to the 19th century, and poets such as Kostis Palamas (because he is classified in the ‘Generation of 1880’), and C. P. Cavafy as he is unique regarding his style and my results would not be clear. The dominance of C.P. Cavafy is also a matter of quantity, as his poets happens to hold the majority of poems into the whole corpus compared to the poems of the other poets (259 poems in total). Thus, the following analysis contains male and female poets from the 20th century. Firstly, I wanted to examine the frequency of words in the whole corpus. So, by using both tools again, AntConc and Stylo(), I got 24,177 word types and 108,571 word tokens (Table ). The twenty most frequent words are mostly function words (articles, prepositions, conjunctions etc.), and the first most frequent word on the text is the conjunction word and.

Poetry Word Types Word Tokens Type/Token ratio (RRT) Female 3,824 20,127 18,90% Male 22,586 98,444 22,94%

Table 12: RRT for male and female Modern Greek poetry

Rank Freq. Word in Greek Word in English Type Category 1 3094 και and Function Conjunction 2 2985 το the [n.singular] Function Article 3 2114 τα the [n.plural] Function Article 4 2077 να to Function Particle, Conjunction 5 1998 που where Content,Function Adverb, Conjunction 6 1998 του of/his [m. singular] Function Article 7 1482 με with Function Preposition 8 1461 η the [f. singular] Function Article 9 1413 κι and Function Conjunction 10 1277 της of/her [f. singular] Function Article 11 1195 ο the [m. singular] Function Article 12 1173 μου my Content Possesive Adjective 13 1112 την the [f. singular] Function Article 14 951 στο at Function Preposition 15 856 τη the [f. singular] Function Article 16 819 τους their/them [m.plural] Function Article 17 805 από from Function Preposition 18 770 σου your Content Possesive Adjective 19 763 τον the [m. singular] Function Article 20 741 σε at Function Preposition

Table 13: The 20 Most Frequent Words of Male and Female Modern Greek Poetry. An English translation is being given

Based on the MFW list, I wanted to see the 10 most frequent nouns for both the male and female poets and highlight any differences or similarities. For this, I created two files, the male and the female poetry file.

S.N. Rank Freq. Word in Greek Word in English 1 46 25 σώμα body 2 50 22 φως light 3 62 16 μάτια eyes 4 63 15 ζωή life 5 64 15 θάνατο death 6 78 13 θάνατος death 7 88 12 ψυχή soul 8 89 11 αίμα blood 9 90 11 ζωής of life 10 93 11 σπίτι house 11 95 11 χέρια hands

Table 14: The 10 Most Frequent Words of Female Modern Greek Poets

S.N. Rank Freq. Word in Greek Word in English 1 45 217 μάτια eyes 2 52 173 φως light 3 57 153 νύχτα night 4 73 121 ζωή life 5 75 118 καρδιά heart 6 76 113 χέρια hands 7 84 103 θάλασσα sea 8 85 103 χρόνια years 9 87 98 ώρα hour 10 97 82 μέρα day

Table 15: The 10 Most Frequent Words of Male Modern Greek Poets

Amongst the most frequent nouns that female poets use are: body/σώμα, light/φως, eyes/μάτια, life/ζωή, and death/θάνατος, while male poets use the words eyes/μάτια, light/φως, night/νύχτα and life/ζωή. It is interesting that the words life and death are dominant words as they appear two times to the table due to declinations. On the contrary, the word death does not appear in the first 10 MFW of male poets. Looking closely to the MFW body on the female poets, I see that the majority of the uses are referring to my body or her body, signifying the need for female poets to express their feminity (see Table 16). Moreover, the ‘Concordance Plot’ tool allowed me to see the difference in frequencies between male and female poetry regarding specific ‘theme-related’ words. Specifically, any related reference to the word love in female poetry is being done with the use of the noun love/έρωτας that has 12 references in total, while in male poetry has 44 references (taking into consideration all the different forms of the noun: έρωτας,-α; έρωτές, έρωτά, έρωτες, έρωτές). If repeated for the word death and its derivatives, as it appears in the most frequent words, I get 34 references for the female poetry and 141 references for the male poetry (again in various forms). In order to compare death with its opposite word (regarding the content) life, I also looked at the occurrence of the word life and its derivatives: thus I have 28 nouns, 7 verbs, 1 adverb, 1 participle and 1 adjective. On the contrary, the male poetry has 177 nouns, 28 verbs (in various tenses) and 1 adjective.

Rank Freq. Range Cluster Word in English 1 6 1 σώμα μου my body 2 6 1 σώμα της her body 3 2 1 σώμα σου your body

Table 16: Cluster types for the word body on female poetry. (Cluster size min. 2; max. 2; search term position: on left; min. Freq. 1; min. Range 1; sorted by frequency).

In the poems by male poets the nouns appear more frequently compared to verbs (in all tenses and parts of speech), adjectives and adverbs. Excluding the main verbs, i.e. the verbs that Greek poets tend to use the most, like the verbs be, have and come, they mostly use verbs of cognition (know, I am aware/ ξέρω, γνωρίζω), of movement (go/πηγαίνω, fall/πέφτω), of senses (listen/ακούω, seeβλέπω), of volition (want/θέλω, desire/επιθυμώ), verbal verbs such as say, verbs of memory (remember/θυμάμαι) and other like rain. Counting the frequency of each verb with the use of ‘Wildcards’ in AntConc (I wrote search queries for all verb occurrences) by including the diverse tenses and any classification of pronouns, possessive determiners, and verb forms, whether they indicate the speaker (first person), the addressee (second person), or a third party (third person), I encountered the following verbs in a variety of tenses and forms. The verb see/βλέπω has 202 occurrences, the verb listen/ακούω 135, the verb know/ξέρω 125, the verb fall/ πέφτω 83, remember/ θυμάμαι 75, the verb say/λέω 247, the verb want/θέλω 112, the verb go/πηγαίνω 125, the verb speak/ μιλώ 102, and the verb rain/βρέχει 36 references. The expression verbs (say, speak) appear the most as well as the verbs of sensation (listen, see) compared to the appearance of verbs of movement or others (for example the verb rains). Therefore, I concluded that male poetry is an expression of personal visions, as well as a way of keeping memories or recall facts from their lives. On the contrary, female poets use a lot of nouns and verbs, compared to adverbs and adjectives. More precisely, amongst the most frequent verbs are: the verb say/λέω (33 references), die/πεθαίνω (23 references), want/θέλω (20 references in 10 different tenses and diverse parts of speech) and can/μπορώ (20 references in 7 different forms). If we contrast the appearance of the verb die to live we get a significant difference (23 to 7 occurrences respectively). Other verbs with their occurrences are the verb see/βλέπω (14), look/κοιτάζω (13), and laugh/γελάω (11 references). Overall, female poets tend to use basic verbs (such as 55

want and see), but the frequent use of the verb die along with its nouns and derivatives signifies the great dominance of death as a subject. Moreover, it is significant that female poets are concerned about their body and its expression. Furthermore, as can be seen from the frequency of the words day, night, and year, as well as the frequency of the word hour(s)/ώρα (-ες) that has 129 references, male poets are significantly concerned about the time and its subdivisions. Moreover, the frequency of the nouns eye(s) and hand(s) signifies the importance of senses and therefore agrees with the above mentioned frequency of the verbs of sensation, but also points out the focus of male poets on the description of the human body (together with the appearance of the noun hair/μαλλιά). The noun heart mostly functions as an expression of feelings (my heart, your heart, the heart of woman) and the frequency of sun and sea(s)/θάλασσα (-ες) (135 references) depicts the importance of nature. If we repeat the same for the word poetry and its derivatives, as we did for the female poets, as well as for the noun woman, we get 84 reference times for poetry and 66 times for the word woman, or women. Especially, for poetry we found 3 different nouns that appear 16 times: poem(s)/ποίημα (-τα), poetry/ποίηση, poet/ποιητής. It is interesting that neither the female poets nor the male poets refer to any female poets. On the contrary, they refer to male poets. The dominance of the noun life over to the noun of death is also important and I wanted to see if the same happens for their derivatives, i.e. to verbs. Thus, I found that the verb die/πεθαίνω appears 91 times, whereas the verb live/ζω appears 28 times. Furthermore, I used the options ‘Keyword list’ and ‘Keyness’ on AntConc and Stylo() for the visualizations in order to do the comparison between male and female poets. ‘Keyness’ allows making comparisons on the frequency of a word between a specific text and a reference corpus. As a reference corpus I first used the file of male poetry and repeated the same for female poetry. More specific, on ‘Tool Preferences’ in AntConc I selected the ‘Keyword List Preferences’ and then clicked on ‘Treat all data as lowercase’. Then, I chose the option ‘Log-likelihood’ (‘Keyword Generation Method’), ‘All values’ (‘Threshold Value’), clicked on the ‘Show negative keywords’ and uploaded the ‘male poetry’ file. Afterwards, I clicked the ‘Keyword List’ tab, chose the option ‘sort by keyness’ and save the outcome to an excel file. The results allowed me to have a comparison between the two files, female and male poetry: the words that have a high positive number ‘keyness’ occur more in the ‘female poetry’ than in the ‘male poetry’ text. On the contrary, the words that have a high negative number ‘keyness’ occur less in the ‘female poetry’ than in the ‘male poetry’ text

(and more on male poetry). Therefore, I looked at the first 20 words with a high positive number and negative number ‘keyness’: the nouns woman and body appear more in female than in male poetry. Moreover, the verbs come, have, ask (in the third grammatical person) also occur more in female than male poets (Table 17a). Amongst the words that have a high negative ‘keyness’ on female poetry (except function words) are the words: mountains/βουνά, hair/μαλλιά, sun/ήλιος, weather/καιρός, you/εσύ, years/χρόνια, high/ψηλά, again/πάλι, hours/ώρες, days/ημέρες. Subsequently, I looked at the first 20 words with a high positive number and negative number ‘keyness’ on male poetry: the nouns sky and music appear more in male than in female poetry, and verbs seem to miss (table 17b).

Rank Freq Keyness Keyword in Greek Keyword in English 1 8 37.955 γυναίκα woman 2 173 35.231 μου my 3 25 32.924 σώμα body 4 269 29.103 να to 5 10 25.780 σχήμα shape 6 7 19.574 θεό God 7 4 18.978 ανέπαφος untouched 8 4 18.978 αριθμός number 9 4 18.978 δικαίωμα right 10 4 18.978 ουσιαστικόν substantive 11 4 18.978 χώρα country 12 5 18.511 ρθω come 13 13 16.518 ποια which 14 14 15.633 έχω have 15 7 15.173 ποίημα poem 16 6 14.287 ρωτάει ask 17 3 14.233 εμένα me 18 3 14.233 mecano mecano 19 3 14.233 αριθμού number 20 3 14.233 γένους gender

Table 17a: The first 20 words on the list that have a high positive ‘keyness’ on female Modern Greek Poetry.

Rank Freq Keyness Keyword in Greek Keyword in English 1 520 11.309 μας our/us 2 37 7.246 είταν was 3 37 7.246 ουρανός sky 4 35 6.854 μουσική music 5 33 6.462 παντού everywhere 6 32 6.267 μικρό small 7 29 5.679 ήλιου sun 8 29 5.679 τραγούδι song 9 27 5.287 κύματα waves 10 27 5.287 πλήθος large number of/crowd 11 27 5.287 σπίτια houses 12 27 5.287 ψωμί bread 13 26 5.092 αγέρας wind 14 52 5.006 βουνά mountains 15 25 4.896 άνθρωπος human 16 25 4.896 κάπου somewhere 17 24 4.700 ρίζες roots 18 23 4.504 καράβι ship 19 66 4.368 μαλλιά hair 20 22 4.308 αστέρια stars

Table 17b: The first 20 words on the list that have a high positive ‘keyness’ on male Modern Greek Poetry.

Figure 6: High negative ‘keyness’ on male Modern Greek Poetry.

Stylo() was also used in order to draw similarities and differences between male and female Greek poets and between different generations. My first attempt was to examine the female writing style and find correlations between the female poets (Fig. 7), and repeat the same for male poets (Fig. 8). For the second analysis I used 124 male poets that have 186,079 tokens. The two visualizations outputted by a Principal Components Analysis depict the correlations between female and male poets respectively. Frantzi’s, Laina’s and Votsi’s writing style appear to a great distance from the other female poets. On the ‘Male poets’ file, the male poets of the 19th century appear away from the centre of the graph, where the majority of poets of the 20th century appear to be.

Figure 7: Principal Component Analysis of female poetry on 100MFW. Figure 8: Principal Component analysis for male poetry of the 19th and the 20th century.

Furthermore, Stylo allowed me to have a visualization of the relation between male and female poetry. I chose the same parameters as before in the GUI, i.e. ‘plain text’ file for ‘Input’, ‘Other’ for Language, and clicked on the ‘UTF-8’, as my text is encoded in Unicode. At ‘Features’ tab I clicked on words and on ‘n-gram size’ I typed ‘1’ as I did not want to find for ‘bigrams’ etc. At ‘MFW Settings’ I chose a value of 50 for ‘minimum’ (so the analysis was done on the 50 most frequent words in the whole corpus) and a value of 100 for ‘maximum’. I chose an increment of 10, that allows me to have an analysis based on 10 20 30 40 50 and so forth and clicked ‘1’ at the ‘Start at freq. rank’.

Figure 9. Bootstrap Consensus tree on the analysis of male and female poetry.

4.2 Conclusion

Overall, male as well as female poets tend to use a lot of nouns and modal verbs (such as have). More precisely, they focus on verbs of senses, like see and verbs of volition (want). Also, the frequent use of the noun death and its derivatives signifies the dominance of death as a subject for both male and female Modern Greek poets. Moreover, it is significant that male poets are concerned about the time whereas female poets tend to speak about their body, but they both equally pay attention to human characteristics. Especially, eye(s), the most frequent noun, has a variety of functions: it functions as a general characteristic of the object of desire marking the feeling of love, as a sign of innocence (the eyes of children), beauty or death, or as a physical characteristic of animals or humans. The dominance of some specific nouns reveals main themes in poetry, such as death, life and love, which appear both in female and male poetry.

5. Literary Names

5.1 Introduction

In general, names operate as an element of recognition and distinction between people, places and objects as they form a mark of identity. In a literary context though, the functions of proper names tend to enlarge, as they include a variety of functions: they might operate as an intertext, a direct or indirect reference to a myth and its meaning, to another poet, author or historical person or even to a random person. The way in which such references are used, detached from the semantic and structural framework that distinguishes them, affects the horizon of the reader’s expectations, who is asked to decode and interpret the new message. Speaking about intertext, it is worth mentioning that in the frame of Intertextuality, any text functions as an open communication system (Kristeva, 1984), as it poses direct or indirect references and allusions to other texts (Worton and Still 1990, 1). For Barthes, intertext is an element that can be recognized and interpreted by the reader, while Genette defined the relationship between the texts as transtextuality, categorizing in intertext (quotation, plagiarism and allusion), paratext (title, subtitle, etc.) palimpsest (literary genres), metatext (comment in another text) and supernatant (parody, travesty, pastiche, caricature). Genette considered that supernatant depends on the ability of the reader, who must recognize the sub-text and decode the relationship between the texts, either satirical or burlesque (Genette 1997). In a modern literary world, intertextuality is a central and influential choice (Plett 1991, 214). The relationship between texts is defined by a quotation, imitation, and creative assimilation or transformation of text-template. So, intertext is characterized by its double referentiality: maintaining the characteristics of the source-text and simultaneously acquiring new characteristics in the new context. Thus, a transforming relationship (i.e. a different function) can exist between the use of the proper name in the source-text and in the meta-text: it might vary from a reference to a real person or a literary hero, to another work (poetic or prosaic), or it might operate as a hint, as a juxtaposition of a myth etc. It might also be the case of a burlesque reuse of a particular proper name (parody: intention to mockery, ridicule or deception) or an intention to rebuild or resurrect the past. According to Linda Hutcheon, parody is revealing the elements that distinguish and link the past and present in

the world of literature, but also the scale of intent, of forms and of targeting (Hutcheon 1995, 93-94). Margaret Rose highlights its comical and intertextual dimension and adds that parody exceeds the terms of imitation and quotation, because rewriting consists in recoding of a literary text (Rose 1993). Specifically, names can sometimes act as markers of intertextuality and therefore might function as indicators of parody or pastiche or might behave symbolically. For example, any names derived from ancient Greek and/or biblical myth might be used to pinpoint the contemporary social and moral fall of man and the paradox of faith. In that case, poets refer to mythological ancient or biblical figures and alter their meaning (Endymion, Muse), or they represent ancient figures in a new, modern way. Except from ancient figures, poets often refer to place names most commonly seen in the biblical myth (Eden) and use them to set the scene of their story, or they might mention other Greek or foreign poets (poets-ancestors etc.). Broadly speaking, the study of proper names and particularly their origin and history is part of Onomastics, Onomastics studies or ‘Name Studies’ (Wikipedia). Specifically, ‘Literary onomastics’ constitute a branch of it that focuses on the study of names in a literary context. Earlier in 1981, Alvarez-Altman published a general guide to literary Onomastics studies by suggesting a typology of names that appear in literary texts. More concretely, though, previous research on the field focuses on “mapping the Onymic Landscape” facilitated by the use of the computer (Van Dalen-Oskam 2006) and language technology (Borin and Kokkinakis 2010); and also concentrated on the study of personal names in novels, where has been proved that novels can be readable even if there is a small number of names used or not at all (Van Dalen-Oskam 2012b). Moreover, the use of names has been studied in the context of ‘comparative literary onomastics’, where different types of names and functions are applied by different authors, in various genres and ways, in different time periods and through different languages (Van Dalen-Oskam 2012a, 2). In particular, Van Dalen-Oskam proved that a quantitative analysis is important when it comes to the study of names in literature and concluded that names are “promising stylistic elements for a comparison across languages” (2012a, 11).

5.2 Names in Modern Greek Poetry

Regarding the function of the names in poetry, it is worth mentioning that their use is slightly different from the function of names in fiction. And that is due to the form of the texts: in fiction, Debus argues, the names aim to “make the fiction more ‘real’ (called the ‘reality- enhancing’ function) (qtd. in van Dalen-Oskam 2012a, 2) or can be divided into plot internal and plot external names (Van Dalen-Oskam 2012a), while in poetry names tend to operate as indicators or markers in order to be given certain meanings regarding a place or person or can be aimed to renew a myth. In terms of this, this chapter aims to retrieve proper names and define their stylistic function in Modern Greek poetry.

5.2.1 Methodology and Findings

First of all, it was important to determine the amount of names that occur in my corpus and secondly classify the names into categories and specify how many names occur in each category. In order to find the names on my corpus I looked at the word list (sorted by word) I got from AntConc. I unified in one lemma any different forms of the same name (plural and singular forms and declinations) as well as any different forms referring to one person, by unifying first name and surname whenever this form refers to the same entity (for example, ‘Anna Komnini’ is classified as one lemma). Furthermore, I decided to limit my analysis to those names that refer directly to a person, mythical hero, geographical place or object. Thus, I excluded National Identity Names, names that “reflect a country, a capital, a city, village, town or suburb, or a national identity” (Alvarez-Altman 1981, 224), for example Indian, Greek etc. Also, I excluded any references to days of the week and months and any appellatives I encountered (such as friend/φίλε and captain/καπετάνε). By using the ‘Word List’ tool in AntConc, I got 1,684 word names and 2,723 word tokens (table 17).

Modern Greek Poetry Word Types Word Tokens 38,982 196,158 Names Word Names Name Tokens 1,684 2,723

Table 18: The total amount of names as Word Names and Name Tokens in Modern Greek Poetry

Subsequently, I divided the names I encountered into three main categories regarding the object of reference: Personal Names, Geographical Names or Place Names (i.e. any references to a country, continent, city, town, street, region, village, river, mountain etc.), and Other Names (i.e. names that cannot be classified to neither of the above mentioned categories, as for example, brand names, astrological signs etc.). The category ‘Personal Names’ was subdivided into five sub-categories regarding the type of name: First Names and surname or family names; Historical Names (references to politicians, historical persons etc.); Mythical Names, subdivided according to their source into Ancient Greek and Biblical Names; Religion Names (i.e. any reference to religion. I included any names that refer to Christian Orthodox persons and names of religious celebrations); and Literary Names (any reference to a literary hero/character, book title etc.). Regarding the category ‘Historical Names’ was also subdivided regarding the occupation of the person. Therefore, I included any reference to author or poets, both Greek and non Greek, to philosophers, emperors, politicians, painters, musicians, etc. It is worth mentioning that some named entities appear into two categories, as their function is diverse. For example, the name ‘Cleopatra’ belongs simultaneously to three categories: as a name of a ship is classified to ‘Other Names’, as a name of a person to ‘Personal Names’ and as a reference to the Queen of Egypt to ‘Historical Names’. The above mentioned division of names was done regarding their content and object of reference. My main interest in analyzing names is to examine their function, as no previous related research has been done in Modern Greek poetry. By using the ‘Concordance Plot’ tool in AntConc, I was able to see the frequency of each name per poet. Each time, I uploaded the related files and typed the name I was looking for in order to have the number of hits per file. Subsequently, I copied the results/number of

hits per name manually and saved them in an excel file. Then I divided the names into the above mentioned Name categories. As I expected, Personal Names appear mostly in Modern Greek poetry, compared to Geographical Names and Other Names (Fig. 10). Looking closely to the frequency of sub-categories of the Personal Names, i.e. the most frequent category of names in Modern Greek Poetry, the First Names and the Historical Names appear the most (Fig. 11).

Figure 10: Occurrences of Names in Modern Greek Poetry per category

Figure 11: Occurrences of Personal Names in Modern Greek Poetry

Except from the main categories and their frequency, I also wanted to find out which of these names are used the most by the majority of the poets. Therefore, the most frequent named entities that are being used by the majority of Modern Greek poets are: Jesus Christ/Ιησούς Χριστός; Greece, Hellas/ Ελλάδα, Ελλάς; Charon/Χάρων; Hades/Άδης; God/Κύριος, Θεός; Saint Mary/Παναγία, Μαρία; Athens/Αθήνα; God/Θεός; Olympus/ Όλυμπος; Aegean Sea/ Αιγαίο (πέλαγος); Odysseus/Οδυσσέας; Easter/Πάσχα; Pleiades/Πούλια; Athena/Αθηνά; Asia/Ασία; Achilles/Αχιλλέας; Crete/Κρήτη; and Rome/Ρώμη (Table ).

Named Entities N. of Poets Jesus Christ/ Ιησούς Χριστός 18 Greece, Hellas/ Ελλάδα, Ελλάς 15 Charon/Χάρων 14 Hades/Άδης 13 God/Κύριος, Θεός 13 Saint Mary/Παναγία, Μαρία 13 Athens/Αθήνα 11 Olympus/ Όλυμπος 11 Aegean Sea/ Αιγαίο (πέλαγος) 8 Odysseus/Οδυσσέας 8 Easter/Πάσχα 7 Pleiades/Πούλια 7

Table 19: Named Entities and number of poets

Subsequently, the top three poets that use the most names are C.P. Cavafy, Nikos Engonopoulos, Andreas Embirikos and Aristotelis Valaoritis. Moreover, the top three names used by the majority of poets for each main category are: Greece, Athens, Olympus (Geographical Names); and for the category Personal Names: Homer, Odysseus Androutsos, Kanaris, Lampros Tzevellas, Aeschylus (Historical Names); Oedipus, Santso, Don Quichote (Literary Names); Charon, Hades, Odysseus (Mythical Names); Jesus Christ, God, Holy Mary (Religion); Helen and Michael (Personal Names).

5.3 Discussion: Function of Names

Looking closely to each category of the names I want to find out the reason why they are used and how they function. Therefore, in this chapter I will briefly refer to the functions of the name categories retrieved in the poetic texts. Geographical Names are usually being used as part of a story, a rewriting of a myth or a tale, constituting the context in which a story unfolds and not being the main theme/subject of the poem. The most frequent function of Geographical Names is the symbolic, i.e. the function that refers to a place that serves as a representation of an idea. Places have the ability to bring into memory strong images and feelings to mind and therefore operate evocatively. For example, the mythologization of a city that represents an ideal that is either lost or one is in constant search of, is the case of Alexandria and Ithaca, two cities that appear in poems of C.P. Cavafy:

The God Abandons Antony When suddenly, at midnight, you hear an invisible procession going by with exquisite music, voices, don’t mourn your luck that’s failing now, work gone wrong, your plans all proving deceptive—don’t mourn them uselessly. As one long prepared, and graced with courage, say goodbye to her, the Alexandria that is leaving. Above all, don’t fool yourself, don’t say it was a dream, your ears deceived you: don’t degrade yourself with empty hopes like these. As one long prepared, and graced with courage, as is right for you who proved worthy of this kind of city, go firmly to the window and listen with deep emotion, but not with the whining, the pleas of a coward; listen—your final delectation—to the voices, to the exquisite music of that strange procession, and say goodbye to her, to the Alexandria you are losing. [Translated by Edmund Keeley/Philip Sherrard]

Geographical names might also function as a means of denunciation of modernization and various social changes, such as alienation. The name of the town sets the story and the street unfolds the main aim of the poet in the following poem of Manolis Anagnostakis:

Thessaloniki, Days of 1969 A.D. In Egyptou Street -first turning right- There now stands the Transaction Bank Building Tourist agencies and emigration bureaus And kids can no longer play with all the traffic passing […] [Translated by David Conolly]

More frequently, Geographical names might function as a memory carrier, a symbol of historical, political, personal or collective memory. This is the case of various cities, mountains, villages etc. appearing in Modern Greek poems, that are connected to national battles and remind the reader of some glorious moments of their history and past. Subsequently, poets often refer to their homeland/ hometown with nostalgia or because they want to recall memories (personal memory). Moreover, Geographical names can operate allusively, where the reader has to find its hidden meaning: the lyric Greece of the Greeks in a poem of Manolis Anagnostakis refers to the Greece of the Christian Greeks, a slogan that the dictatorship used during the years 1967-1974. It can be also the case that places reveal parts of a poet’s life, as is the case of Nikos Kavvadias, who due to his occupation (sailor), travelled a lot and visited exotic places. In order to study the function of Mythical names in Modern Greek poetry, it is important to mention general aspects of myth. One of the basic properties of the myth is its flexibility: the ability to transform depending on the social, economic and political conditions, to adapt every time in a new aesthetic and cultural environment and to amend depending on the personality and poetics of each author (Pageaux 1994, 95-112). The intertextual transformations of myth represent the variety of different and revamped versions of it: it creates a communication and interactive relationship between previous and subsequent texts, from author to author (ancestor and descendants or between modern writers), with the various mythical versions to be specified in addition to the recruitment of the individual reader (both over time and cross-sectional). The intertexts in the case of the myth play an important role as they constitute an element that facilitates the author to impose

a particular logic of his message, but is respectively a brilliant opportunity for the reader to show the strength of his own intervention in the production of the myth. Contemporary literature and poetry affirms the non-static function of myth, because the myth is being rewritten and transformed in its narrative structures in a comical, burlesque, or ironic way. Keeping that in mind, mythical names operate as intertextual markers that signify a transformation or rewriting of a particular myth or they are being used to emphasize the message that the poet wants to communicate. Myth can be found in a poem’s content, where themes and patterns are maintained or changed, and where behavior or acts of mythical figures and mythical events are altered. The reference to persons and heroes of mythology is being given nominally (Artemis), in the form of invocation (Oh, Zeus) or through metaphors. Many times mythical elements are used into the body of the poem individually and intertwined conceptually with the theme of the poem. Schematically, references to myth can be distinguished in relation to their function a) in a simple-symbolic (metaphor and transfer) without subversive mood; and b) with subversive playful mood. Personal names, first names and surnames, fulfil a realistic function, where the poet aims to strengthen his relationship with the reader by serving immediacy and simplicity in meanings. In that case, personal names function either to show the main character of a story in the poem, to present a person as a thorough example of bravery, to be part of a dialogue that the poet unfolds and that takes place between him/her and another person. Historical names are usually used to present the persons that formulated grand historical events, either to be presented as random people with their weaknesses or to be given as persons of great glory and uniqueness. Especially, C.P. Cavafy who based a great number of his poems to present known and less known historical persons usually refers to them with irony or subversion. Moreover, he mentions the beauty and youth of historical persons as an exemplar of beauty and builds his poem on various stories about these persons. Literary names used in the context of Modern Greek poems usually operate metaphorically, as the poets refer to them in order to make connections with their own time period, situation or even personality. In that case, poets usually act with sarcasm and irony, as happens in the case of the poets Kostas Karyotakis and Romos Filyras. Overall, the use of names in Modern Greek poetry indicate a tense of rewriting a story, an event or myth, referring to simple people and their stories and operate as markers of intertextuality, that can either reveal stylistic features such as irony and subversion or indicate relations between poets. For this research, the use of digital tools was of great significance as

it gave me the opportunity to have a quantitative approach to a large set of texts and have an overview of the use of names in Modern Greek poetry, both qualitatively and quantitatively.

6. Conclusion

6.1 Aim

It is true that as we change “our platforms, tools, and technologies” (Burdick et al. 2012, 86), they also inevitably change us, as they transform the way we think, the way we nowadays search for various information and communicate and they inevitably change the way knowledge is being produced. Digital tools, in particular, shift the structure of methodology; they reconstruct it, by providing new possibilities of conducting research and algorithms promise to facilitate research through suggesting new methods and ways for analyzing data. Digital tools contribute significantly to the interpretation of a text, for example by giving the opportunity to the scholar to find the frequency of a word across literary texts and draw conclusions based on the results he/she has. Traditional Humanities and Digital Humanities are different, as Hayles asserts (2012b), meaning that each one of them has different perspectives and possibilities. Digital Humanities is “a natural outgrowth and expansion of the traditional scope of the Humanities, not a replacement or rejection of humanistic inquiry. In fact, the role of the humanist is critical at this historic moment, as our cultural legacy migrates to digital formats and our relation to knowledge, cultural material, technology, and society is radically re- conceptualized.” (Presner and Johanson 2009, 2). Regarding the ‘close reading’ of poetry, this is based on identifying characteristics of a poem and its association with a variety of features, linguistic, literary, sociological, historical, etc. As Coles and Gonnering (2013) assert “close readers engage texts directly, intimately and in detail; they trace the finest interactions among such literary features as rhyme and meter, sound, figures, and syntax, noting how even the subtlest movements and operations (a comma, a repeated vowel, etc.) influence a reader’s interpretation(s) and experience(s) of a particular poem.”. With the use of software tools it is possible for the researcher to detect and examine stylistic features in a set of poetic texts and analyze aspects of them that otherwise would be impossible to track. More precisely, algorithms and software tools assist in the interpretation and text analysis of poetic texts, for example by illustrating the frequency of words that occur. In terms of this, there is a combination of both close and distant reading.

The aim of this study is to prove the importance of quantitative computer-assisted analysis in the study of Modern Greek poetry. Via digital tools it was possible to analyze a large amount of poetic texts and retrieve relations and differences between poets classified in diverse poetic generations. It was proved that Modern Greek poets use nouns rather than adjectives and that the majority of them refer to parts of human body and senses. Moreover, the frequency of particular nouns revealed dominant subjects of Modern Greek poetry, such as ‘death’ and ‘life’. It is also significant that male poets are concerned about the time whereas female poets tend to speak about their body. Moreover, by retrieving and analyzing the use of names in poetic texts by comparing their appearance and function, contributed in revealing aspects of Modern Greek poetry, such as the depiction and importance of places in poetry or the intertextuality. I believe that the study of Modern Greek poetry has a lot to benefit by adding quantitative approach next to qualitative as well as to move on to computational stylistic analysis to larger corpora of texts. I do hope that similar studies will be specified in the future.

6.2 Suggestion for Future Research

As female poetry proved to be generally underrepresented on the web, my suggestion is to create a digital poetic archive, dedicated to female poetry, which will include a representative sample of their poets in Greek and will provide English translations to poems. The same can also be done for main Modern Greek poetic generations, such as the ‘Generation of the 70s’. The creation of these types of digital archives will promote the study of Modern Greek poetry and highlight the significance of Modern Greek female poetry. Moreover, it would be of great importance to study literary names into a large scale of literary texts, and specifically to poetry, as this seems to reveal various aspects of Modern Greek Poetry and unfold relationships between poets of different generations and gender. Of great significance is to study the translation of Greek names that appear on poems into other languages and mark differences and/or similarities between languages. Overall, I believe that computational stylistics has a lot to give to the study of Modern Greek Poetry once it begins to be used broadly.

Notes

1 The term ‘euphuistic style’ signifies the style of the English author John Lyly, which is characterized by the use of alliterations, repetitions, antitheses etc. (Schoeck and Patrick 1966, 241-244). 2 Imagism is an early 20th century movement of poetry on which poems present poets’ impressions of visual objects into common speech (Abrams 1999, 122-23). 3 Last Access on February 2016. 4 Script can be downloaded from: https://sites.google.com/site/computationalstylistics/

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