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) (1)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 (1)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