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Introduction to Lexical Semantics

Introduction to Lexical Semantics. Vasileios Hatzivassiloglou University of Texas at Dallas. What this course is about. Recent advances in NLP Advances in the area of “lexical semantics” Semantics = meaning Lexical = related to words. Language Constraints.

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Introduction to Lexical Semantics

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  1. Introduction to Lexical Semantics Vasileios Hatzivassiloglou University of Texas at Dallas

  2. What this course is about • Recent advances in NLP • Advances in the area of “lexical semantics” • Semantics = meaning • Lexical = related to words

  3. Language Constraints • Several mechanisms operate to control allowable messages in a language and their meaning • Basic block: a letter / grapheme • Letters combine to form morphemes (e.g., re-) and words

  4. Types of constraints • Men dogs walks (syntax) • Colorless green ideas sleep furiously (semantics) • The stock market made a gain (lexical preferences) • Discourse/pragmatics • inference, missing information, implicature, appropriateness

  5. Word meaning • Partly compositional (derivations) • Mostly arbitrary • Also not unique, in many ways • How to represent a word’s meaning?

  6. Meaning representation • Logical form • Attributes / properties • Relationships with other words • Specialization • Synonymy • Opposition • Meronymy

  7. Polysemy • Multiple meanings for a word • A central issue for interpreting/understanding text

  8. Contrastive Polysemy Weinreich (1964) • a. The bank of the river b. The richest bank in the city (2) a. The defendant approached the bar b. The defendant was in the pub at the bar 25+ senses of bar

  9. Complementary Polysemy • The bank raised interest rates yesterday. The store is next to the new bank. (2) Mary painted the door. Mary walked through the door. (3) Sam enjoyed the lamb. The lamb is running on the field.

  10. Metaphor and Metonymy • All the world's a stage, And all the men and women merely players They have their exits and their entrances • The White House said ... • The pen is mightier than the sword

  11. Synecdoche, Allegory, Hyperbole • Synecdoche • Part for whole: • head for cattle • Whole for part: • the police, the Pentagon • Species for genus: • kleenex • Genus for species: • PC

  12. Main Questions • How can we model lexical semantics? • Discuss properties or attributes relating to word meaning, constraints on word use • How can we learn those properties and constraints? • What can we use them for? • Focus on applications in bioinformatics

  13. Dictionaries • Representing meaning via definitions, examples • Core vocabulary • The problem of circular reference • Automated construction

  14. Ontologies • Representing word meaning via inheritance/specialization • Manual and automated construction • Domain vs. general ontologies • Specific ontologies (PenMan, SENSUS)

  15. Lexical Databases • Representing meaning via intersections of concepts and links (semantic nets) • WordNet, manual construction and verification • Automating lexical relationship extraction • Multiple languages

  16. Context as a means for determining lexical relationships • A word is known by the company it keeps • Statistical tests for word use, compositional preferences • Measures for coincidence, estimation issues

  17. Disambiguation • Selecting among multiple meanings • Dictionary and corpus-based approaches • Training and avoiding training data • Evaluations (SENSEVAL) • Role of domain and discourse • Multiple levels

  18. Non-compositional preferences • Collocations • Non-compositional (kick the bucket) • Non-substitutable (white wine) • Non-transformable • Types of collocations • How to find them • Domain specialization, translation

  19. Lexical properties • Lexical relationships (specialization, synonymy, antonymy, meronymy) • Orientation • Markedness • Domain/register applicability

  20. Semantic Similarity • Used for classification, organization, clustering • Vector representations of context • Similarity based on vector comparison, probabilistic models, LSI • Robustness and bias • Clustering and content-based smoothing

  21. Orientation and Ordering • Semantic orientation or polarity • Lexical vs. document level (review) • Semantic strength • Linguistic scales and implicature

  22. Text mining • Using large quantities of unnanotated text for learning lexical properties • The web as corpus

  23. Mapping across languages • Static mapping (bilingual dictionaries) • Dynamic mapping in MT • Interlingua representations • Statistical transfer

  24. Evaluation Issues • Suitable reference standards • Agreement between evaluators • Avoiding bias

  25. Selectional constraints • Preposition/Article selection • Text generation • Lexical cohesion (for rewriting, but also for selecting words) • math/statistics vs. math/food

  26. Terminology • Deciding what is a term • Terminological databases • Issues of consistency, reference concepts, currency, coverage • Automatic detection of terms • Constraining and classifying terms • Definitions for terms

  27. Bioinformatics • Emerging field • Meaning of technical terms • Disambiguation (e.g., protein/gene) • Classification • Functional roles • Abbreviations

  28. List of topics • Dictionaries, ontologies, databases • Measures for word coincidence, similarity • Disambiguation • Collocations • Word categorization and clustering • Orientation and ordering • Text mining, the web as corpus • Evaluation • Multilingual issues • Selectional constraints and cohesion • Terminology • Bioinformatics

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