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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

Introduction to Lexical Semantics

Vasileios Hatzivassiloglou

University of Texas at Dallas

what this course is about
What this course is about
  • Recent advances in NLP
  • Advances in the area of “lexical semantics”
  • Semantics = meaning
  • Lexical = related to words
language constraints
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
types of constraints
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
word meaning
Word meaning
  • Partly compositional (derivations)
  • Mostly arbitrary
  • Also not unique, in many ways
  • How to represent a word’s meaning?
meaning representation
Meaning representation
  • Logical form
  • Attributes / properties
  • Relationships with other words
    • Specialization
    • Synonymy
    • Opposition
    • Meronymy
  • Multiple meanings for a word
  • A central issue for interpreting/understanding text
contrastive polysemy
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

complementary polysemy
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.

metaphor and metonymy
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
synecdoche allegory hyperbole
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
main questions
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
  • Representing meaning via definitions, examples
  • Core vocabulary
  • The problem of circular reference
  • Automated construction
  • Representing word meaning via inheritance/specialization
  • Manual and automated construction
  • Domain vs. general ontologies
  • Specific ontologies (PenMan, SENSUS)
lexical databases
Lexical Databases
  • Representing meaning via intersections of concepts and links (semantic nets)
  • WordNet, manual construction and verification
  • Automating lexical relationship extraction
  • Multiple languages
context as a means for determining lexical relationships
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
  • Selecting among multiple meanings
  • Dictionary and corpus-based approaches
  • Training and avoiding training data
  • Evaluations (SENSEVAL)
  • Role of domain and discourse
  • Multiple levels
non compositional preferences
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
lexical properties
Lexical properties
  • Lexical relationships (specialization, synonymy, antonymy, meronymy)
  • Orientation
  • Markedness
  • Domain/register applicability
semantic similarity
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
orientation and ordering
Orientation and Ordering
  • Semantic orientation or polarity
  • Lexical vs. document level (review)
  • Semantic strength
  • Linguistic scales and implicature
text mining
Text mining
  • Using large quantities of unnanotated text for learning lexical properties
  • The web as corpus
mapping across languages
Mapping across languages
  • Static mapping (bilingual dictionaries)
  • Dynamic mapping in MT
  • Interlingua representations
  • Statistical transfer
evaluation issues
Evaluation Issues
  • Suitable reference standards
  • Agreement between evaluators
  • Avoiding bias
selectional constraints
Selectional constraints
  • Preposition/Article selection
  • Text generation
  • Lexical cohesion (for rewriting, but also for selecting words)
    • math/statistics vs. math/food
  • 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
  • Emerging field
  • Meaning of technical terms
  • Disambiguation (e.g., protein/gene)
  • Classification
  • Functional roles
  • Abbreviations
list of topics
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