What Is a Compound? the Main Criteria for Compoundhood
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Department of English and American Studies English Language And
Masaryk University Faculty of Arts Department of English and American Studies English Language and Literature Jan Štěrba Word-formation Processes in TV Series Bachelor’s Diploma Thesis Supervisor: doc. PhDr. Naděžda Kudrnáčová, Csc. 2015 I declare that I have worked on this thesis independently, using only the primary and secondary sources listed in the bibliography. …………………………………………….. Jan Štěrba I would like to thank my supervisor doc, PhDr. Naděžda Kudrnáčová, Csc., for her time, kindness and willingness to help. Table of Contents Introduction ....................................................................................................................... 6 1. Word-formation ............................................................................................................ 9 1.1 Definitions of Basic Terms ..................................................................................... 9 1.1.1 Lexeme, word-form, derivation and inflection ................................................. 9 1.1.2 Root, stem and base ........................................................................................ 10 1.2 Definition of Word-formation ............................................................................... 11 1.3 Productivity and Lexicalization ............................................................................ 12 2. Word-formation Processes in English ........................................................................ 13 2.1 Compounding ....................................................................................................... -
Compound Word Formation.Pdf
Snyder, William (in press) Compound word formation. In Jeffrey Lidz, William Snyder, and Joseph Pater (eds.) The Oxford Handbook of Developmental Linguistics . Oxford: Oxford University Press. CHAPTER 6 Compound Word Formation William Snyder Languages differ in the mechanisms they provide for combining existing words into new, “compound” words. This chapter will focus on two major types of compound: synthetic -ER compounds, like English dishwasher (for either a human or a machine that washes dishes), where “-ER” stands for the crosslinguistic counterparts to agentive and instrumental -er in English; and endocentric bare-stem compounds, like English flower book , which could refer to a book about flowers, a book used to store pressed flowers, or many other types of book, as long there is a salient connection to flowers. With both types of compounding we find systematic cross- linguistic variation, and a literature that addresses some of the resulting questions for child language acquisition. In addition to these two varieties of compounding, a few others will be mentioned that look like promising areas for coordinated research on cross-linguistic variation and language acquisition. 6.1 Compounding—A Selective Review 6.1.1 Terminology The first step will be defining some key terms. An unfortunate aspect of the linguistic literature on morphology is a remarkable lack of consistency in what the “basic” terms are taken to mean. Strictly speaking one should begin with the very term “word,” but as Spencer (1991: 453) puts it, “One of the key unresolved questions in morphology is, ‘What is a word?’.” Setting this grander question to one side, a word will be called a “compound” if it is composed of two or more other words, and has approximately the same privileges of occurrence within a sentence as do other word-level members of its syntactic category (N, V, A, or P). -
Hunspell – the Free Spelling Checker
Hunspell – The free spelling checker About Hunspell Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex word compounding or character encoding. Hunspell interfaces: Ispell-like terminal interface using Curses library, Ispell pipe interface, OpenOffice.org UNO module. Main features of Hunspell spell checker and morphological analyzer: - Unicode support (affix rules work only with the first 65535 Unicode characters) - Morphological analysis (in custom item and arrangement style) and stemming - Max. 65535 affix classes and twofold affix stripping (for agglutinative languages, like Azeri, Basque, Estonian, Finnish, Hungarian, Turkish, etc.) - Support complex compoundings (for example, Hungarian and German) - Support language specific features (for example, special casing of Azeri and Turkish dotted i, or German sharp s) - Handle conditional affixes, circumfixes, fogemorphemes, forbidden words, pseudoroots and homonyms. - Free software (LGPL, GPL, MPL tri-license) Usage The src/tools dictionary contains ten executables after compiling (or some of them are in the src/win_api): affixcompress: dictionary generation from large (millions of words) vocabularies analyze: example of spell checking, stemming and morphological analysis chmorph: example of automatic morphological generation and conversion example: example of spell checking and suggestion hunspell: main program for spell checking and others (see manual) hunzip: decompressor of hzip format hzip: compressor of -
Building a Treebank for French
Building a treebank for French £ £¥ Anne Abeillé£ , Lionel Clément , Alexandra Kinyon ¥ £ TALaNa, Université Paris 7 University of Pennsylvania 75251 Paris cedex 05 Philadelphia FRANCE USA abeille, clement, [email protected] Abstract Very few gold standard annotated corpora are currently available for French. We present an ongoing project to build a reference treebank for French starting with a tagged newspaper corpus of 1 Million words (Abeillé et al., 1998), (Abeillé and Clément, 1999). Similarly to the Penn TreeBank (Marcus et al., 1993), we distinguish an automatic parsing phase followed by a second phase of systematic manual validation and correction. Similarly to the Prague treebank (Hajicova et al., 1998), we rely on several types of morphosyntactic and syntactic annotations for which we define extensive guidelines. Our goal is to provide a theory neutral, surface oriented, error free treebank for French. Similarly to the Negra project (Brants et al., 1999), we annotate both constituents and functional relations. 1. The tagged corpus pronoun (= him ) or a weak clitic pronoun (= to him or to As reported in (Abeillé and Clément, 1999), we present her), plus can either be a negative adverb (= not any more) the general methodology, the automatic tagging phase, the or a simple adverb (= more). Inflectional morphology also human validation phase and the final state of the tagged has to be annotated since morphological endings are impor- corpus. tant for gathering constituants (based on agreement marks) and also because lots of forms in French are ambiguous 1.1. Methodology with respect to mode, person, number or gender. For exam- 1.1.1. -
Prefixes and Suffixes
им от medical roots, suffixes and prefi... ■ 2.5 Roots of quantity j _ • - • ' I * 3 See also j Prefixes and suffixes The following is an alphabetical list o f medical prefixes and suffixes, along with their meanings, origin, and an English example. A P re fix o r 1 M e a n in g | Origin language and etymology Example (s) su ffix j i1 Ancient Greek a-/ccv- (a-/an-), | a-, an - Denotes an absence of ! Apathy, Analgia without, not ... ........... ' i iab - aw ay from Latin A bduction i Latin (abdomen), abdomen, fat abdomin(o)- O f or relating to the abdomen A bdom en ! around the belly cardiac, hydrophobiac, -ac, -a c a l pertaining to Greek -ако<; (-akos) pharmacomaniacal . Ancient Greek racavGa (akantha), acanthioa acanthocyte, aca n th (o )- thorn or spine thorn acanthoma, acanthulus Greek акоосгакос; (acoustikos), " i aco u s(io )- O f or relating to hearing acoumeter, acoustician o f or for hearing ■ Greek axpov (akron), highest or Acrocrany, acromegaly, acr(o )- extremity, topmost larthest point acroosteolysis, acroposthia Greek акоостко*; (acoustikos), -acu sis hearing paracusis o f or for hearing ' -a d tow ard, in the direction o f dorsad increase, adherence, motion ad - Latin A dduction toward, very ad en (o )-, Ancient Greek d8f|v, a8ev- Adenocarcinoma, adenology, i O f or relating to a gland ad en (i)- (aden, aden-), an acorn; a gland jadenotome, adenotyphus | O f or relating to lat or fatty adip(o)- Latin (adeps, adip-), lat A dipocyte tissue j | i O f or relating to adrenal i adren(o)- , Latin Iadrenal artery glands j -a p . -
Compounds and Word Trees
Compounds and word trees LING 481/581 Winter 2011 Organization • Compounds – heads – types of compounds • Word trees Compounding • [root] [root] – machine gun – land line – snail mail – top-heavy – fieldwork • Notice variability in punctuation (to ignore) Compound stress • Green Lake • bluebird • fast lane • Bigfoot • bad boy • old school • hotline • Myspace Compositionality • (Extent to which) meanings of words, phrases determined by – morpheme meaning – structure • Predictability of meaning in compounds – ’roid rage (< 1987; ‘road rage’ < 1988) – football (< 1486; ‘American football’ 1879) – soap opera (< 1939) Head • ‘A word in a syntactic construction or a morpheme in a morphological one that determines the grammatical function or meaning of the construction as a whole. For example, house is the head of the noun phrase the red house...’ Aronoff and Fudeman 2011 Righthand head rule • In English (and most languages), morphological heads are the rightmost non- inflectional morpheme in the word – Lexical category of entire compound = lexical category of rightmost member – Not Spanish (HS example p. 139) • English be- – devil, bedevil Compounding and lexical category noun verb adjective noun machine friend request skin-deep gun foot bail rage quit verb thinktank stir-fry(?) ? adjective high school dry-clean(?) red-hot low-ball ‘the V+N pattern is unproductive and limited to a few lexically listed items, and the N+V pattern is not really productive either.’ HS: 138 Heads of words • Morphosyntactic head: determines lexical category – syntactic distribution • That thinktank is overrated. – morphological characteristics; e.g. inflection • plurality: highschool highschools • tense on V: dry-clean dry-cleaned • case on N – Sahaptin wáyxti- “run, race” wayxtitpamá “pertaining to racing” wayxtitpamá k'úsi ‘race horse’ Máytski=ish á-shapaxwnawiinknik-xa-na wayxtitpamá k'úsi-nan.. -
A Poor Man's Approach to CLEF
A poor man's approach to CLEF Arjen P. de Vries1;2 1 CWI, Amsterdam, The Netherlands 2 University of Twente, Enschede, The Netherlands [email protected] October 16, 2000 1 Goals The Mirror DBMS [dV99] aims specifically at supporting both data management and content man- agement in a single system. Its design separates the retrieval model from the specific techniques used for implementation, thus allowing more flexibility to experiment with a variety of retrieval models. Its design based on database techniques intends to support this flexibility without caus- ing a major penalty on the efficiency and scalability of the system. The support for information retrieval in our system is presented in detail in [dVH99], [dV98], and [dVW99]. The primary goal of our participation in CLEF is to acquire experience with supporting Dutch users. Also, we want to investigate whether we can obtain a reasonable performance without requiring expensive (but high quality) resources. We do not expect to obtain impressive results with our system, but hope to obtain a baseline from which we can develop our system further. We decided to submit runs for all four target languages, but our main interest is in the bilingual Dutch to English runs. 2 Pre-processing We have used only ‘off-the-shelf' tools for stopping, stemming, compound-splitting (only for Dutch) and translation. All our tools are available for free, without usage restrictions for research purposes. Stopping and stemming Moderately sized stoplists, of comparable coverage, were made available by University of Twente (see also Table 1). We used the stemmers provided by Muscat1, an open source search engine. -
Robust Prediction of Punctuation and Truecasing for Medical ASR
Robust Prediction of Punctuation and Truecasing for Medical ASR Monica Sunkara Srikanth Ronanki Kalpit Dixit Sravan Bodapati Katrin Kirchhoff Amazon AWS AI, USA fsunkaral, [email protected] Abstract the accuracy of subsequent natural language under- standing algorithms (Peitz et al., 2011a; Makhoul Automatic speech recognition (ASR) systems et al., 2005) to identify information such as pa- in the medical domain that focus on transcrib- tient diagnosis, treatments, dosages, symptoms and ing clinical dictations and doctor-patient con- signs. Typically, clinicians explicitly dictate the versations often pose many challenges due to the complexity of the domain. ASR output punctuation commands like “period”, “add comma” typically undergoes automatic punctuation to etc., and a postprocessing component takes care enable users to speak naturally, without hav- of punctuation restoration. This process is usu- ing to vocalise awkward and explicit punc- ally error-prone as the clinicians may struggle with tuation commands, such as “period”, “add appropriate punctuation insertion during dictation. comma” or “exclamation point”, while true- Moreover, doctor-patient conversations lack ex- casing enhances user readability and improves plicit vocalization of punctuation marks motivating the performance of downstream NLP tasks. This paper proposes a conditional joint mod- the need for automatic prediction of punctuation eling framework for prediction of punctuation and truecasing. In this work, we aim to solve the and truecasing using pretrained masked lan- problem of automatic punctuation and truecasing guage models such as BERT, BioBERT and restoration to medical ASR system text outputs. RoBERTa. We also present techniques for Most recent approaches to punctuation and true- domain and task specific adaptation by fine- casing restoration problem rely on deep learning tuning masked language models with medical (Nguyen et al., 2019a; Salloum et al., 2017). -
Lexical Analysis
Lexical Analysis Lecture 3 Instructor: Fredrik Kjolstad Slide design by Prof. Alex Aiken, with modifications 1 Outline • Informal sketch of lexical analysis – Identifies tokens in input string • Issues in lexical analysis – Lookahead – Ambiguities • Specifying lexers (aka. scanners) – By regular expressions (aka. regex) – Examples of regular expressions 2 Lexical Analysis • What do we want to do? Example: if (i == j) Z = 0; else Z = 1; • The input is just a string of characters: \tif (i == j)\n\t\tz = 0;\n\telse\n\t\tz = 1; • Goal: Partition input string into substrings – Where the substrings are called tokens 3 What’s a Token? • A syntactic category – In English: noun, verb, adjective, … – In a programming language: Identifier, Integer, Keyword, Whitespace, … 4 Tokens • A token class corresponds to a set of strings Infinite set • Examples i var1 ports – Identifier: strings of letters or foo Person digits, starting with a letter … – Integer: a non-empty string of digits – Keyword: “else” or “if” or “begin” or … – Whitespace: a non-empty sequence of blanks, newlines, and tabs 5 What are Tokens For? • Classify program substrings according to role • Lexical analysis produces a stream of tokens • … which is input to the parser • Parser relies on token distinctions – An identifier is treated differently than a keyword 6 Designing a Lexical Analyzer: Step 1 • Define a finite set of tokens – Tokens describe all items of interest • Identifiers, integers, keywords – Choice of tokens depends on • language • design of parser 7 Example • Recall \tif -
Also Called Back-Derivation, Retrograde Derivation Or Deaffixation) Is Often Described As One of the Minor Word-Formation Processes
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DSpace at University of West Bohemia 2013 ACTA UNIVERSITATIS CAROLINAE PAG. 31–60 PHILOLOGICA 3 / PRAGUE STUDIES IN ENGLISH XXVI BACK-FORMATION IN THE NEWEST LAYER OF ENGLISH VOCABULARY NADĚŽDA STAŠKOVÁ 1. Introduction Back-formation (also called back-derivation, retrograde derivation or deaffixation) is often described as one of the minor word-formation processes. It has a special position among the other types of word-formation for several reasons. Firstly, it is a process direc- tionally opposite in its character to the most frequent, and possibly most natural way of forming new words in English – affixation. In back-formation, instead of being added, an affix (or that part of the source word which is supposed to be an affix) is subtracted, which thus results in a shorter or morphologically less complex item of vocabulary. Comparison of the two processes is given in Table 1. The verb ovate (1988) represents the most typical and modern result of back-formation – it is a verb formed from an action noun and its meaning is “to give someone an ovation; applaud and cheer enthusiastically”. Table 1: Comparison of affixation and back-formation SOURCE WORD > RESULTING WORD AFFIXATION: translate > translation BACK-FORMATION: ovation > ovate Especially because of the opposite direction of creation, back-formation is often regard- ed as an unusual (or even unnatural) process, and new back-formed words are at their first occurrence frequently felt as weird, humorous or inacceptable. The truth is that some back-formations appear only as nonce words, sometimes deliberately coined by a speak- er for humorous, ironic, or otherwise idiosyncratic, purposes and do not have a long life. -
The Process of Compound Words Used in Martin's
THE PROCESS OF COMPOUND WORDS USED IN MARTIN’S NOVEL A GAME OF THRONES A THESIS BY BONIFASIA BR KARO REG. NO. 150721022 DEPARTMENT OF ENGLISH FACULTY OF CULTURAL STUDIES UNIVERSITY OF SUMATERA UTARA MEDAN 2019 UNIVERSITAS SUMATERA UTARA THE PROCESS OF COMPOUND WORDS USED IN MARTIN’S NOVEL A GAME OF THRONES A THESIS BY BONIFASIA BR KARO REG. NO. 150721022 SUPERVISOR CO-SUPERVISOR Dr. Deliana, M.Hum. Liza Amalia Putri, S.S., MA NIP. 19571117 198303 2 002 NIP. 198406262017042001 Submitted to Faculty of Cultural Studies University of Sumatera Utara Medan in partial fulfillment of the requirements for the degree of Sarjana Sastra from Department of English DEPARTMENT OF ENGLISH FACULTY OF CULTURAL STUDIES UNIVERSITY OF SUMATERA UTARA MEDAN 2019 UNIVERSITAS SUMATERA UTARA Approved by the Department of English, Faculty of Cultural Studies University of Sumatera Utara (USU) Medan as thesis for The Sarjana Sastra Examination. Head, Secretary, Prof. T. Silvana Sinar, M.A., Ph.D Rahmadsyah Rangkuti, M.A., Ph.D NIP. 1954091619803 2 003 NIP. 19750209 200812 1 002 UNIVERSITAS SUMATERA UTARA Accepted by the Board of Examiners in partial fulfillment of requirements for the degree of Sarjana Sastra from the Department of English, Faculty of Cultural Studies University of Sumatera Utara, Medan. The examination is held in Department of English Faculty of Cultural Studies University of Sumatera Utara on Augt 16th, 2019. Dean of Faculty of Cultural Studies University of Sumatera Utara Dr. Budi Agustono, M.S. NIP.19600805 198703 1 001 Board of Examiners Rahmadsyah Rangkuti, M.A. Ph.D Dr. Deliana, M.Hum Dr. -
Etymological Wordnet: Tracing the History of Words
Etymological Wordnet: Tracing The History of Words Gerard de Melo IIIS, Tsinghua University Beijing, P.R. China [email protected] Abstract Research on the history of words has led to remarkable insights about language and also about the history of human civilization more generally. This paper presents the Etymological Wordnet, the first database that aims at making word origin information available as a large, machine-readable network of words in many languages. The information in this resource is obtained from Wiktionary. Extracting a network of etymological information from Wiktionary requires significant effort, as much of the etymological information is only given in prose. We rely on custom pattern matching techniques and mine a large network with over 500,000 word origin links as well as over 2 million derivational/compositional links. Keywords: etymology, historical linguistics, multilingual resources 1. Introduction 2. Background Investigating the origins of words can lead to remarkable In the 19th century, numerous connections between Indo- insights about the cultural background that has shaped the European languages were recognized, resulting in impor- semantics of our modern vocabulary. As a matter of fact, tant insights that fundamentally shaped linguistics and an- research in comparative and historical linguistics has not thropology. For instance, English “ten”, German “zehn”, only produced numerous invaluable findings about the his- Latin “decem”, Greek “deka”, and Sanskrit “dasa´ ” are all tory of words and languages but also about the history of cognates, i.e., words that descend from the same Proto- humanity and the migration patterns that have shaped our Indo-European ancestor. Due to various phonetic, phono- world.