Corpus Linguistics Main Issues
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Talk Bank: a Multimodal Database of Communicative Interaction
Talk Bank: A Multimodal Database of Communicative Interaction 1. Overview The ongoing growth in computer power and connectivity has led to dramatic changes in the methodology of science and engineering. By stimulating fundamental theoretical discoveries in the analysis of semistructured data, we can to extend these methodological advances to the social and behavioral sciences. Specifically, we propose the construction of a major new tool for the social sciences, called TalkBank. The goal of TalkBank is the creation of a distributed, web- based data archiving system for transcribed video and audio data on communicative interactions. We will develop an XML-based annotation framework called Codon to serve as the formal specification for data in TalkBank. Tools will be created for the entry of new and existing data into the Codon format; transcriptions will be linked to speech and video; and there will be extensive support for collaborative commentary from competing perspectives. The TalkBank project will establish a framework that will facilitate the development of a distributed system of allied databases based on a common set of computational tools. Instead of attempting to impose a single uniform standard for coding and annotation, we will promote annotational pluralism within the framework of the abstraction layer provided by Codon. This representation will use labeled acyclic digraphs to support translation between the various annotation systems required for specific sub-disciplines. There will be no attempt to promote any single annotation scheme over others. Instead, by promoting comparison and translation between schemes, we will allow individual users to select the custom annotation scheme most appropriate for their purposes. -
Machine-Translation Inspired Reordering As Preprocessing for Cross-Lingual Sentiment Analysis
Master in Cognitive Science and Language Master’s Thesis September 2018 Machine-translation inspired reordering as preprocessing for cross-lingual sentiment analysis Alejandro Ramírez Atrio Supervisor: Toni Badia Abstract In this thesis we study the effect of word reordering as preprocessing for Cross-Lingual Sentiment Analysis. We try different reorderings in two target languages (Spanish and Catalan) so that their word order more closely resembles the one from our source language (English). Our original expectation was that a Long Short Term Memory classifier trained on English data with bilingual word embeddings would internalize English word order, resulting in poor performance when tested on a target language with different word order. We hypothesized that the more the word order of any of our target languages resembles the one of our source language, the better the overall performance of our sentiment classifier would be when analyzing the target language. We tested five sets of transformation rules for our Part of Speech reorderings of Spanish and Catalan, extracted mainly from two sources: two papers by Crego and Mariño (2006a and 2006b) and our own empirical analysis of two corpora: CoStEP and Tatoeba. The results suggest that the bilingual word embeddings that we are training our Long Short Term Memory model with do not improve any English word order learning by part of the model when used cross-lingually. There is no improvement when reordering the Spanish and Catalan texts so that their word order more closely resembles English, and no significant drop in result score even when applying a random reordering to them making them almost unintelligible, neither when classifying between 2 options (positive-negative) nor between 4 (strongly positive, positive, negative, strongly negative). -
Usage of IT Terminology in Corpus Linguistics Mavlonova Mavluda Davurovna
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-7S, May 2019 Usage of IT Terminology in Corpus Linguistics Mavlonova Mavluda Davurovna At this period corpora were used in semantics and related Abstract: Corpus linguistic will be the one of latest and fast field. At this period corpus linguistics was not commonly method for teaching and learning of different languages. used, no computers were used. Corpus linguistic history Proposed article can provide the corpus linguistic introduction were opposed by Noam Chomsky. Now a day’s modern and give the general overview about the linguistic team of corpus. corpus linguistic was formed from English work context and Article described about the corpus, novelties and corpus are associated in the following field. Proposed paper can focus on the now it is used in many other languages. Alongside was using the corpus linguistic in IT field. As from research it is corpus linguistic history and this is considered as obvious that corpora will be used as internet and primary data in methodology [Abdumanapovna, 2018]. A landmark is the field of IT. The method of text corpus is the digestive modern corpus linguistic which was publish by Henry approach which can drive the different set of rules to govern the Kucera. natural language from the text in the particular language, it can Corpus linguistic introduce new methods and techniques explore about languages that how it can inter-related with other. for more complete description of modern languages and Hence the corpus linguistic will be automatically drive from the these also provide opportunities to obtain new result with text sources. -
Collection of Usage Information for Language Resources from Academic Articles
Collection of Usage Information for Language Resources from Academic Articles Shunsuke Kozaway, Hitomi Tohyamayy, Kiyotaka Uchimotoyyy, Shigeki Matsubaray yGraduate School of Information Science, Nagoya University yyInformation Technology Center, Nagoya University Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan yyyNational Institute of Information and Communications Technology 4-2-1 Nukui-Kitamachi, Koganei, Tokyo, 184-8795, Japan fkozawa,[email protected], [email protected], [email protected] Abstract Recently, language resources (LRs) are becoming indispensable for linguistic researches. However, existing LRs are often not fully utilized because their variety of usage is not well known, indicating that their intrinsic value is not recognized very well either. Regarding this issue, lists of usage information might improve LR searches and lead to their efficient use. In this research, therefore, we collect a list of usage information for each LR from academic articles to promote the efficient utilization of LRs. This paper proposes to construct a text corpus annotated with usage information (UI corpus). In particular, we automatically extract sentences containing LR names from academic articles. Then, the extracted sentences are annotated with usage information by two annotators in a cascaded manner. We show that the UI corpus contributes to efficient LR searches by combining the UI corpus with a metadata database of LRs and comparing the number of LRs retrieved with and without the UI corpus. 1. Introduction Thesaurus1. In recent years, such language resources (LRs) as corpora • He also employed Roget’s Thesaurus in 100 words of and dictionaries are being widely used for research in the window to implement WSD. -
Linguistic Annotation of the Digital Papyrological Corpus: Sematia
Marja Vierros Linguistic Annotation of the Digital Papyrological Corpus: Sematia 1 Introduction: Why to annotate papyri linguistically? Linguists who study historical languages usually find the methods of corpus linguis- tics exceptionally helpful. When the intuitions of native speakers are lacking, as is the case for historical languages, the corpora provide researchers with materials that replaces the intuitions on which the researchers of modern languages can rely. Using large corpora and computers to count and retrieve information also provides empiri- cal back-up from actual language usage. In the case of ancient Greek, the corpus of literary texts (e.g. Thesaurus Linguae Graecae or the Greek and Roman Collection in the Perseus Digital Library) gives information on the Greek language as it was used in lyric poetry, epic, drama, and prose writing; all these literary genres had some artistic aims and therefore do not always describe language as it was used in normal commu- nication. Ancient written texts rarely reflect the everyday language use, let alone speech. However, the corpus of documentary papyri gets close. The writers of the pa- pyri vary between professionally trained scribes and some individuals who had only rudimentary writing skills. The text types also vary from official decrees and orders to small notes and receipts. What they have in common, though, is that they have been written for a specific, current need instead of trying to impress a specific audience. Documentary papyri represent everyday texts, utilitarian prose,1 and in that respect, they provide us a very valuable source of language actually used by common people in everyday circumstances. -
MASC: the Manually Annotated Sub-Corpus of American English
MASC: The Manually Annotated Sub-Corpus of American English Nancy Ide*, Collin Baker**, Christiane Fellbaum†, Charles Fillmore**, Rebecca Passonneau†† *Vassar College Poughkeepsie, New York USA **International Computer Science Institute Berkeley, California USA †Princeton University Princeton, New Jersey USA ††Columbia University New York, New York USA E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] Abstract To answer the critical need for sharable, reusable annotated resources with rich linguistic annotations, we are developing a Manually Annotated Sub-Corpus (MASC) including texts from diverse genres and manual annotations or manually-validated annotations for multiple levels, including WordNet senses and FrameNet frames and frame elements, both of which have become significant resources in the international computational linguistics community. To derive maximal benefit from the semantic information provided by these resources, the MASC will also include manually-validated shallow parses and named entities, which will enable linking WordNet senses and FrameNet frames within the same sentences into more complex semantic structures and, because named entities will often be the role fillers of FrameNet frames, enrich the semantic and pragmatic information derivable from the sub-corpus. All MASC annotations will be published with detailed inter-annotator agreement measures. The MASC and its annotations will be freely downloadable from the ANC website, thus providing -
Segmentability Differences Between Child-Directed and Adult-Directed Speech: a Systematic Test with an Ecologically Valid Corpus
Report Segmentability Differences Between Child-Directed and Adult-Directed Speech: A Systematic Test With an Ecologically Valid Corpus Alejandrina Cristia 1, Emmanuel Dupoux1,2,3, Nan Bernstein Ratner4, and Melanie Soderstrom5 1Dept d’Etudes Cognitives, ENS, PSL University, EHESS, CNRS 2INRIA an open access journal 3FAIR Paris 4Department of Hearing and Speech Sciences, University of Maryland 5Department of Psychology, University of Manitoba Keywords: computational modeling, learnability, infant word segmentation, statistical learning, lexicon ABSTRACT Previous computational modeling suggests it is much easier to segment words from child-directed speech (CDS) than adult-directed speech (ADS). However, this conclusion is based on data collected in the laboratory, with CDS from play sessions and ADS between a parent and an experimenter, which may not be representative of ecologically collected CDS and ADS. Fully naturalistic ADS and CDS collected with a nonintrusive recording device Citation: Cristia A., Dupoux, E., Ratner, as the child went about her day were analyzed with a diverse set of algorithms. The N. B., & Soderstrom, M. (2019). difference between registers was small compared to differences between algorithms; it Segmentability Differences Between Child-Directed and Adult-Directed reduced when corpora were matched, and it even reversed under some conditions. Speech: A Systematic Test With an Ecologically Valid Corpus. Open Mind: These results highlight the interest of studying learnability using naturalistic corpora Discoveries in Cognitive Science, 3, 13–22. https://doi.org/10.1162/opmi_ and diverse algorithmic definitions. a_00022 DOI: https://doi.org/10.1162/opmi_a_00022 INTRODUCTION Supplemental Materials: Although children are exposed to both child-directed speech (CDS) and adult-directed speech https://osf.io/th75g/ (ADS), children appear to extract more information from the former than the latter (e.g., Cristia, Received: 15 May 2018 2013; Shneidman & Goldin-Meadow,2012). -
Creating and Using Multilingual Corpora in Translation Studies Claudio Fantinuoli and Federico Zanettin
Chapter 1 Creating and using multilingual corpora in translation studies Claudio Fantinuoli and Federico Zanettin 1 Introduction Corpus linguistics has become a major paradigm and research methodology in translation theory and practice, with practical applications ranging from pro- fessional human translation to machine (assisted) translation and terminology. Corpus-based theoretical and descriptive research has investigated written and interpreted language, and topics such as translation universals and norms, ideol- ogy and individual translator style (Laviosa 2002; Olohan 2004; Zanettin 2012), while corpus-based tools and methods have entered the curricula at translation training institutions (Zanettin, Bernardini & Stewart 2003; Beeby, Rodríguez Inés & Sánchez-Gijón 2009). At the same time, taking advantage of advancements in terms of computational power and increasing availability of electronic texts, enormous progress has been made in the last 20 years or so as regards the de- velopment of applications for professional translators and machine translation system users (Coehn 2009; Brunette 2013). The contributions to this volume, which are centred around seven European languages (Basque, Dutch, German, Greek, Italian, Spanish and English), add to the range of studies of corpus-based descriptive studies, and provide exam- ples of some less explored applications of corpus analysis methods to transla- tion research. The chapters, which are based on papers first presented atthe7th congress of the European Society of Translation Studies held in Germersheim in Claudio Fantinuoli & Federico Zanettin. 2015. Creating and using multilin- gual corpora in translation studies. In Claudio Fantinuoli & Federico Za- nettin (eds.), New directions in corpus-based translation studies, 1–11. Berlin: Language Science Press Claudio Fantinuoli and Federico Zanettin July/August 20131, encompass a variety of research aims and methodologies, and vary as concerns corpus design and compilation, and the techniques used to ana- lyze the data. -
From CHILDES to Talkbank
From CHILDES to TalkBank Brian MacWhinney Carnegie Mellon University MacWhinney, B. (2001). New developments in CHILDES. In A. Do, L. Domínguez & A. Johansen (Eds.), BUCLD 25: Proceedings of the 25th annual Boston University Conference on Language Development (pp. 458-468). Somerville, MA: Cascadilla. a similar article appeared as: MacWhinney, B. (2001). From CHILDES to TalkBank. In M. Almgren, A. Barreña, M. Ezeizaberrena, I. Idiazabal & B. MacWhinney (Eds.), Research on Child Language Acquisition (pp. 17-34). Somerville, MA: Cascadilla. Recent years have seen a phenomenal growth in computer power and connectivity. The computer on the desktop of the average academic researcher now has the power of room-size supercomputers of the 1980s. Using the Internet, we can connect in seconds to the other side of the world and transfer huge amounts of text, programs, audio and video. Our computers are equipped with programs that allow us to view, link, and modify this material without even having to think about programming. Nearly all of the major journals are now available in electronic form and the very nature of journals and publication is undergoing radical change. These new trends have led to dramatic advances in the methodology of science and engineering. However, the social and behavioral sciences have not shared fully in these advances. In large part, this is because the data used in the social sciences are not well- structured patterns of DNA sequences or atomic collisions in super colliders. Much of our data is based on the messy, ill-structured behaviors of humans as they participate in social interactions. Categorizing and coding these behaviors is an enormous task in itself. -
The British National Corpus Revisited: Developing Parameters for Written BNC2014 Abi Hawtin (Lancaster University, UK)
The British National Corpus Revisited: Developing parameters for Written BNC2014 Abi Hawtin (Lancaster University, UK) 1. The British National Corpus 2014 project The ESRC Centre for Corpus Approaches to Social Science (CASS) at Lancaster University and Cambridge University Press are working together to create a new, publicly accessible corpus of contemporary British English. The corpus will be a successor to the British National Corpus, created in the early 1990s (BNC19941). The British National Corpus 2014 (BNC2014) will be of the same order of magnitude as BNC1994 (100 million words). It is currently projected that the corpus will reach completion in mid-2018. Creation of the spoken sub-section of BNC2014 is in its final stages; this paper focuses on the justification and development of the written sub- section of BNC2014. 2. Justification for BNC2014 There are now many web-crawled corpora of English, widely available to researchers, which contain far more data than will be included in Written BNC2014. For example, the English TenTen corpus (enTenTen) is a web-crawled corpus of Written English which currently contains approximately 19 billion words (Jakubíček et al., 2013). Web-crawling processes mean that this huge amount of data can be collected very quickly; Jakubíček et al. (2013: 125) note that “For a language where there is plenty of material available, we can gather, clean and de-duplicate a billion words a day.” So, with extremely large and quickly-created web-crawled corpora now becoming commonplace in corpus linguistics, the question might well be asked why a new, 100 million word corpus of Written British English is needed. -
Multimedia Corpora (Media Encoding and Annotation) (Thomas Schmidt, Kjell Elenius, Paul Trilsbeek)
Multimedia Corpora (Media encoding and annotation) (Thomas Schmidt, Kjell Elenius, Paul Trilsbeek) Draft submitted to CLARIN WG 5.7. as input to CLARIN deliverable D5.C3 “Interoperability and Standards” [http://www.clarin.eu/system/files/clarindeliverableD5C3_v1_5finaldraft.pdf] Table of Contents 1 General distinctions / terminology................................................................................................................................... 1 1.1 Different types of multimedia corpora: spoken language vs. speech vs. phonetic vs. multimodal corpora vs. sign language corpora......................................................................................................................................................... 1 1.2 Media encoding vs. Media annotation................................................................................................................... 3 1.3 Data models/file formats vs. Transcription systems/conventions.......................................................................... 3 1.4 Transcription vs. Annotation / Coding vs. Metadata ............................................................................................. 3 2 Media encoding ............................................................................................................................................................... 5 2.1 Audio encoding ..................................................................................................................................................... 5 2.2 -
Gold Standard Annotations for Preposition and Verb Sense With
Gold Standard Annotations for Preposition and Verb Sense with Semantic Role Labels in Adult-Child Interactions Lori Moon Christos Christodoulopoulos Cynthia Fisher University of Illinois at Amazon Research University of Illinois at Urbana-Champaign [email protected] Urbana-Champaign [email protected] [email protected] Sandra Franco Dan Roth Intelligent Medical Objects University of Pennsylvania Northbrook, IL USA [email protected] [email protected] Abstract This paper describes the augmentation of an existing corpus of child-directed speech. The re- sulting corpus is a gold-standard labeled corpus for supervised learning of semantic role labels in adult-child dialogues. Semantic role labeling (SRL) models assign semantic roles to sentence constituents, thus indicating who has done what to whom (and in what way). The current corpus is derived from the Adam files in the Brown corpus (Brown, 1973) of the CHILDES corpora, and augments the partial annotation described in Connor et al. (2010). It provides labels for both semantic arguments of verbs and semantic arguments of prepositions. The semantic role labels and senses of verbs follow Propbank guidelines (Kingsbury and Palmer, 2002; Gildea and Palmer, 2002; Palmer et al., 2005) and those for prepositions follow Srikumar and Roth (2011). The corpus was annotated by two annotators. Inter-annotator agreement is given sepa- rately for prepositions and verbs, and for adult speech and child speech. Overall, across child and adult samples, including verbs and prepositions, the κ score for sense is 72.6, for the number of semantic-role-bearing arguments, the κ score is 77.4, for identical semantic role labels on a given argument, the κ score is 91.1, for the span of semantic role labels, and the κ for agreement is 93.9.