1. Introduction
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The Generative Lexicon
The Generative Lexicon James Pustejovsky" Computer Science Department Brandeis University In this paper, I will discuss four major topics relating to current research in lexical seman- tics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best ap- proaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is per- formed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole. 1. Introduction I believe we have reached an interesting turning point in research, where linguistic studies can be informed by computational tools for lexicology as well as an appre- ciation of the computational complexity of large lexical databases. -
CS460/626 : Natural Language Processing/Speech, NLP and the Web
CS460/626 : Natural Language Processing/Speech, NLP and the Web Lecture 24, 25, 26 Wordnet Pushpak Bhattacharyya CSE Dept., IIT Bombay 17th and 19th (morning and night), 2013 NLP Trinity Problem Semantics NLP Trinity Parsing Part of Speech Tagging Morph Analysis Marathi French HMM Hindi English Language CRF MEMM Algorithm NLP Layer Discourse and Corefernce Increased Semantics Extraction Complexity Of Processing Parsing Chunking POS tagging Morphology Background Classification of Words Word Content Function Word Word Verb Noun Adjective Adverb Prepo Conjun Pronoun Interjection sition ction NLP: Thy Name is Disambiguation A word can have multiple meanings and A meaning can have multiple words Word with multiple meanings Where there is a will, Where there is a will, There are hundreds of relatives Where there is a will There is a way There are hundreds of relatives A meaning can have multiple words Proverb “A cheat never prospers” Proverb: “A cheat never prospers but can get rich faster” WSD should be distinguished from structural ambiguity Correct groupings a must … Iran quake kills 87, 400 injured When it rains cats and dogs run for cover Should be distinguished from structural ambiguity Correct groupings a must … Iran quake kills 87, 400 injured When it rains, cats and dogs runs for cover When it rains cats and dogs, run for cover Groups of words (Multiwords) and names can be ambiguous Broken guitar for sale, no strings attached (Pun) Washington voted Washington to power pujaa ne pujaa ke liye phul todaa (Pujaa plucked -
Introduction to Wordnet: an On-Line Lexical Database
Introduction to WordNet: An On-line Lexical Database George A. Miller, Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine Miller (Revised August 1993) WordNet is an on-line lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets. Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to ®nd the word they are looking for. But a frequent objection to this solution is that ®nding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because ®nding the information would interrupt their work and break their train of thought. In this age of computers, however, there is an answer to that complaint. One obvious reason to resort to on-line dictionariesÐlexical databases that can be read by computersÐis that computers can search such alphabetical lists much faster than people can. A dictionary entry can be available as soon as the target word is selected or typed into the keyboard. Moreover, since dictionaries are printed from tapes that are read by computers, it is a relatively simple matter to convert those tapes into the appropriate kind of lexical database. -
Leveraging Morpho-Semantics for the Discovery of Relations in Chinese Wordnet
Leveraging Morpho-semantics for the Discovery of Relations in Chinese Wordnet Shu-Kai Hsieh Yu-Yun Chang Graduate Institute of Linguistics Graduate Institute of Linguistics National Taiwan University National Taiwan University Taipei, Taiwan Taipei, Taiwan [email protected] [email protected] Abstract data from the web (Cimiano et al., 2005), Semantic relations of different types have but runs the risk of influenced by the web played an important role in wordnet, and have genre (Alain, 2010). been widely recognized in various fields. In re- To enrich the relations coverage in Chinese cent years, with the growing interests of con- structing semantic network in support of in- Wordnet (CWN), in this paper, we propose telligent systems, automatic semantic relation an in situ approach by exploiting the morph- discovery has become an urgent task. This semantic information. This method, simple paper aims to extract semantic relations re- lying on the in situ morpho-semantic struc- and straightforward as it seems, does not incur ture in Chinese which can dispense of an the difficulties associated with lexical gaps in outside source such as corpus or web data. cross-language mapping that any translation- Manual evaluation of thousands of word pairs shows that most relations can be successful based model would encounter; and it is also predicted. We believe that it can serve as a economic and complementary with previous valuable starting point in complementing with approaches in that we can dispense of an out- other approaches, which will hold promise for the robust lexical relations acquisition. side corpus resource. In what follows, Section 2 gives a brief sum- 1 Introduction mary of lexical semantic relations acquisition Semantic relations are at the core of WordNet- from two perspectives. -
Automatic Labeling of Troponymy for Chinese Verbs
Automatic labeling of troponymy for Chinese verbs 羅巧Ê Chiao-Shan Lo*+ s!蓉 Yi-Rung Chen+ [email protected] [email protected] 林芝Q Chih-Yu Lin+ 謝舒ñ Shu-Kai Hsieh*+ [email protected] [email protected] *Lab of Linguistic Ontology, Language Processing and e-Humanities, +Graduate School of English/Linguistics, National Taiwan Normal University Abstract 以同©^Æ與^Y語意關¶Ë而成的^Y知X«，如ñ語^² (Wordnet)、P語^ ² (EuroWordnet)I，已有E分的研v，^²的úË_已øv完善。ú¼ø同的目的，- 研b語言@¦已úË'規!K-文^Y²路 (Chinese Wordnet,CWN)，è(Ð供完t的 -文YK^©@分。6而，(目MK-文^Y²路ûq-，1¼目M;要/¡(ºº$ 定來標記同©^ÆK間的語意關Â，因d這些標記KxÏ尚*T成可L應(K一定規!。 因d，,Ç文章y%針對動^K間的上下M^Y語意關 (Troponymy)，Ðú一.ê動標 記的¹法。我們希望藉1句法上y定的句型 (lexical syntactic pattern)，úË一個能 ê 動½取ú動^上下M的ûq。透N^©意$定原G的U0，P果o:，dûqê動½取ú 的動^上M^，cº率將近~分K七A。,研v盼能將,¹法應(¼c(|U-的-文^ ²ê動語意關Â標記，以Ê知X,體Kê動úË，2而能有H率的úË完善的-文^Y知 XÇ源。 關關關uuu^^^：-文^Y²路、語©關Âê動標記、動^^Y語© Abstract Synset and semantic relation based lexical knowledge base such as wordnet, have been well-studied and constructed in English and other European languages (EuroWordnet). The Chinese wordnet (CWN) has been launched by Academia Sinica basing on the similar paradigm. The synset that each word sense locates in CWN are manually labeled, how- ever, the lexical semantic relations among synsets are not fully constructed yet. In this present paper, we try to propose a lexical pattern-based algorithm which can automatically discover the semantic relations among verbs, especially the troponymy relation. There are many ways that the structure of a language can indicate the meaning of lexical items. For Chinese verbs, we identify two sets of lexical syntactic patterns denoting the concept of hypernymy-troponymy relation. -
Part 4: Lexical Semantics 2
Natural Language Processing Part 4: lexical semantics 2 Marco Maggini Tecnologie per l'elaborazione del linguaggio Lexical semantics • A lexicon generally has a highly structured form ▫ It stores the meanings and uses of each word ▫ It encodes the relations between words and meanings • A lexeme is the minimal unit represented in the lexicon ▫ It pairs a stem (the orthographic/phonological form chosen to represent words) with a symbolic form for meaning representation (sense) • A dictionary is a kind of lexicon where meanings are expressed through definitions and examples son noun a boy or man in relation to either or both of his parents. • a male offspring of an animal. • a male descendant : the sons of Adam. • ( the Son) (in Christian belief) the second person of the Trinity; Christ. • a man considered in relation to his native country or area : one of Nevada's most famous sons. • a man regarded as the product of a particular person, influence, or environment : sons of the French Revolution. • (also my son) used by an elder person as a form of address for a boy or young man : “You're on private land, son.” 3 Marco Maggini Tecnologie per l'elaborazione del linguaggio Lexicons & dictionaries • Definitions in dictionaries exploit words and they may be circular (a word definition uses words whose definitions exploit that word) right adj. 1. of, relating to, situated on, or being the side of the body which is away from the side on which the heart is mostly located 2. located nearer to the right hand than to the left 3. -
Bootstrap” Into Syntax?*
Cognirion. 45 (1992) 77-100 What sort of innate structure is needed to “bootstrap” into syntax?* Martin D.S. Braine Department of Psychology, New York University, 6 Washington Place. 8th Floor, New York. NY 10003, LISA Received March 5. 1990, final revision accepted March 13, 1992 Abstract Braine, M.D.S., 1992. What sort of innate structure is needed to “bootstrap” into syntax? Cognition, 45: 77-100 The paper starts from Pinker’s theory of the acquisition of phrase structure; it shows that it is possible to drop all the assumptions about innate syntactic structure from this theory. These assumptions can be replaced by assumptions about the basic structure of semantic representation available at the outset of language acquisition, without penalizing the acquisition of basic phrase structure rules. Essentially, the role played by X-bar theory in Pinker’s model would be played by the (presumably innate) structure of the language of thought in the revised parallel model. Bootstrapping and semantic assimilation theories are shown to be formally very similar,. though making different primitive assumptions. In their primitives, semantic assimilation theories have the advantage that they can offer an account of the origin of syntactic categories instead of postulating them as primitive. Ways of improving on the semantic assimilation version of Pinker’s theory are considered, including a way of deriving the NP-VP constituent division that appears to have a better fit than Pinker’s to evidence on language variation. Correspondence to: Martin Braine, Department of Psychology, New York University, 6 Washing- ton Place. 8th Floor, New York, NY 10003. -
Learning Antonyms with Paraphrases and A
LEARNING ANTONYMS WITH PARAPHRASES AND A MORPHOLOGY-AWARE NEURAL NETWORK Sneha Rajana A THESIS In Computer and Information Science Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Master of Science in Engineering 2017 _______________________________ Prof. Chris Callison-Burch Supervisor of Thesis _______________________________ Prof. Lyle Ungar Thesis Reader _______________________________ Prof. Boon Thau-Loo Masters Chairperson Acknowledgements I would like to thank Prof. Chris Callison-Burch for not only supervising this thesis but for being an amazing advisor and for getting me interested in NLP research. I am thankful to Prof. Lyle Ungar for serving on the thesis committee and Prof. Boon Thau Loo and the CIS department of Penn Engineering for providing me with the en- couragement and necessary resources to carry out this project. Besides this, I would also like to thank Vered Shwartz, Bar-Ilan University, Israel for providing us with the preprocessed corpus and software for parts of this work and whose insightful advice and guidance led to the successful completion of this project. I am grateful to my fellow Masters students whose constant encouragement and support was invaluable. Finally, thank you to my family. I am who I am because of you. ii Abstract Recognizing and distinguishing antonyms from other types of semantic relations is a key part of language understanding systems and has widespread applications in Natural Language Processing tasks. In this study, we present two novel methods for identifying antonyms. In our first method, we use paraphrase pairs containing negation markers to derive antonym pairs. -
Introduction
Published in M. Bowerman and P. Brown (Eds.), 2008 Crosslinguistic Perspectives on Argument Structure: Implications for Learnability, p. 1-26. Majwah, NJ: Lawrence Erlbaum CHAPTER 1 Introduction Melissa Bowerman and Penelope Brown Max Planck Institute for Psycholinguistics Verbs are the glue that holds clauses together. As elements that encode events, verbs are associated with a core set of semantic participants mat take part in the event. Some of a verb's semantic participants, although not necessarily all, are mapped to roles that are syntactically relevant in the clause, such as subject or direct object; these are the arguments of the verb. For example, in John kicked the ball, 'John' and 'the ball' are semantic participants of the verb kick, and they are also its core syntactic arguments—the subject and the direct object, respectively. Another semantic participant, 'foot', is also understood, but it is not an argument; rather, it is incorpo- rated directly into the meaning of the verb. The array of participants associated with verbs and other predicates, and how these participants are mapped to syntax, are the focus of the Study of ARGUMENT STRUCTURE. At one time, argument structure meant little more than the number of arguments appearing with a verb, for example, one for an intransitive verb, two for a transitive verb. But argument structure has by now taken on a central theoretical position in the study of both language structure and language development. In linguistics, ar- gument structure is seen as a critical interface between the lexical semantic properties of verbs and the morphosyntactic properties of the clauses in which they appear (e.g., Grimshaw, 1990; Goldberg, 1995; Hale & Keyser, 1993; Levin & Rappaport Hovav, 1995; Jackendoff, 1990). -
Representations of Troponymy and Verb Hypernymy in BERT
University of Helsinki Faculty of Arts Department of Digital Humanities Master’s Thesis BERT, have you no manners? Representations of troponymy and verb hypernymy in BERT Dmitry Narkevich 015093853 Master’s Programme in Linguistic Diversity and Digital Humanities Supervisor: Marianna Apidianaki 11.05.2021 Tiedekunta – Fakultet – Faculty Koulutusohjelma – Utbildningsprogram – Degree Programme Faculty of Arts Master's Programme in Linguistic Diversity and Digital Humanities Opintosuunta – Studieinriktning – Study Track Language Technology Tekijä – Författare – Author Dmitry Narkevich Työn nimi – Arbetets titel – Title BERT, have you no manners? : Representations of troponymy and verb hypernymy in BERT Työn laji – Arbetets art – Level Aika – Datum – Month and Sivumäärä– Sidoantal – Number of pages Master’s Thesis year 43 May 2021 Tiivistelmä – Referat – Abstract Hypernymy is a relationship between two words, where the hyponym carries a more specific meaning, and entails a hypernym that carries a more general meaning. A particular kind of verbal hypernymy is troponymy, where troponyms are verbs that encode a particular manner or way of doing something, such as “whisper” meaning “to speak in a quiet manner”. Recently, contextualized word vectors have emerged as a powerful tool for representing the semantics of words in a given context, in contrast to earlier static embeddings where every word is represented by a single vector regardless of sense. BERT, a pre-trained language model that uses contextualized word representations, achieved state of the art performance on various downstream NLP tasks such as question answering. Previous research identified knowledge of scalar adjective intensity in BERT, but not systematic knowledge of nominal hypernymy. In this thesis, we investigate systematic knowledge of troponymy and verbal hypernymy in the base English version of BERT. -
Computational Modeling of Human Language Acquisition Copyright © 2011 by Morgan & Claypool
SeriesSeries ISSN: ISSN: 1947-4040 1947-4040 ALISHAHI ALISHAHI ALISHAHI SSYNTHESISYNTHESIS LLECTURESECTURES ONON MM MorganMorgan& & ClaypoolClaypool PublishersPublishers HHUMANUMAN L LANGUAGEANGUAGE T TECHNOLOGIESECHNOLOGIES &&CC SeriesSeries Editor: Editor: Graeme Graeme Hirst, Hirst, University University of of Toronto Toronto ComputationalComputational Modeling Modeling of of Human Human Language Language Acquisition Acquisition COMPUTATIONAL MODELING OF HUMAN LANGUAGE ACQUISITION COMPUTATIONAL MODELING COMPUTATIONAL OF MODELING HUMAN OF LANGUAGE HUMAN ACQUISITION LANGUAGE ACQUISITION AfraAfra Alishahi, Alishahi, University University of of Saarlandes Saarlandes ComputationalComputational ModelingModeling HumanHuman language language acquisition acquisition has has been been studied studied for for centuries, centuries, but but using using computational computational modeling modeling for for such such studiesstudiesstudies is isis a a arelatively relativelyrelatively recent recentrecent trend. trend.trend. However, However,However, computational computationalcomputational approaches approachesapproaches to toto language languagelanguage learning learninglearning have havehave become becomebecome ofof HumanHuman LanguageLanguage increasinglyincreasinglyincreasingly popular, popular,popular, mainly mainlymainly due duedue to toto advances advancesadvances in inin developing developingdeveloping machine machinemachine learning learninglearning techniques, techniques,techniques, and andand the thethe availability availabilityavailability -
Bootstrapping Into Filler-Gap: an Acquisition Story
Bootstrapping into Filler-Gap: An Acquisition Story Marten van Schijndel Micha Elsner The Ohio State University vanschm,melsner @ling.ohio-state.edu { } Abstract Age 13mo 15mo 20mo 25mo Wh-S No Yes Yes Yes Analyses of filler-gap dependencies usu- ally involve complex syntactic rules or (Yes) heuristics; however recent results suggest Wh-O No Yes Yes that filler-gap comprehension begins ear- lier than seemingly simpler constructions 1-1 Yes No such as ditransitives or passives. Therefore, this work models filler-gap acquisition as a byproduct of learning word orderings (e.g. Figure 1: The developmental timeline of subject SVO vs OSV), which must be done at a (Wh-S) and object (Wh-O) wh-clause extraction very young age anyway in order to extract comprehension suggested by experimental results meaning from language. Specifically, this (Seidl et al., 2003; Gagliardi et al., 2014). Paren- model, trained on part-of-speech tags, rep- theses indicate weak comprehension. The final row resents the preferred locations of semantic shows the timeline of 1-1 role bias errors (Naigles, roles relative to a verb as Gaussian mix- 1990; Gertner and Fisher, 2012). Missing nodes de- tures over real numbers. note a lack of studies. This approach learns role assignment in filler-gap constructions in a manner con- sistent with current developmental findings acquired through learning word orderings rather and is extremely robust to initialization than relying on hierarchical syntactic knowledge. variance. Additionally, this model is shown to be able to account for a characteristic er- This work describes a cognitive model of the de- ror made by learners during this period (A velopmental timecourse of filler-gap comprehension and B gorped interpreted as A gorped B).