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Lexical Semantics Chapter 16

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Lexeme pairing of an orthographic or phonological representation with a ... synonymy: {chump, fish, fool, gull, mark, patsy, fall guy, sucker, schlemiel, ... – PowerPoint PPT presentation

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Title: Lexical Semantics Chapter 16


1
Lexical SemanticsChapter 16
  • Lindsay Butler
  • Ling 538
  • 5 December 2006

2
What is lexical semantics?
  • Systematic meaning-related structure
  • Lexeme pairing of an orthographic or
    phonological representation with a meaning
    (Saussurian sign)
  • Lexicon a list of lexemes (finite)
  • Sense the meaning component of a lexeme
  • Lexemes are not analyzable units. They have
    internal structure that determines how they
    combine with other elements in the sentence
  • The lexicon is not simply a finite listing, but
    rather a creative generator of infinite meanings

3
Senses of lexemes
  • Homophony words that have the same form but
    different meaning
  • bank financial institution
  • bank sloping mound
  • Homograph (orthographic)
  • bass type of fish
  • bass musical instrument
  • versus homophone (phonological)
  • would auxiliary verb
  • wood hard, fibrous substance

4
Senses of lexemes
  • Polysemy a single lexeme with multiple related
    meanings
  • bank (financial institution) and bank (sloping
    mound) are not related (etymologically)
  • but, bank (financial institution) and
  • blood bank (not financial, but same concept of
    holding a deposit, just of blood)
  • You can bank on Mans (not financial, but it has
    the sense of security)
  • Finding the right meaning is the task of word
    sense disambiguation

5
Senses of lexemes
  • Synonymy different lexemes with the same
    meaning
  • Test of substitutability
  • Example big and large

6
Senses of lexemes
  • Hyponymy a class of synonymy pairings of
    lexemes where one denotes a subclass of the other
  • Hyponym the more general of the pair
  • Car is a hyponym of vehicle
  • Hypernym the more specific of the pair
  • Set of hyponyms have proved useful approximations
    of ontologies, taxonomies, and object structures

7
WordNet
  • A database of lexical relations for English
  • http//wordnet.princeton.edu
  • Three databases for nouns, verbs, adjectives and
    adverbs
  • Based on the concept of a synset
  • synonymy chump, fish, fool, gull, mark, patsy,
    fall guy, sucker, schlemiel, shlemiel, soft
    touch, mug a person who is gullible and easy to
    take advantage of

8
Structure of lexemes
  • Thematic roles a set of categories that
    characterize certain arguments of verbs into a
    shallow semantic language
  • Jon climbed the wall
  • Shannon washed his hands
  • Deep roles are specific to the event climb, wash
  • Shallow roles reveal a commonality between
    climbing and washing They have animate
    volitional actors that are causers of the event.
    Thus, they demonstrate the thematic role of agent

9
More thematic roles
  • Theme participant most directly affected
  • Experiencer simply, the experiencer
  • Force non-volitional causer
  • Instrument simply, an instrument used
  • Beneficiary simply, the beneficiary
  • Source origin of the object of a transfer
  • Goal destination of the object of a transfer

10
FrameNet
  • Lexical resource for English thematic roles
    (Baker et al., 1998 Lowe et al., 1997)
  • http//framenet.icsi.berkeley.edu
  • More than 625 semantic frames.

11
Structure of lexemes
  • Selectional restrictions
  • Lexemes have restrictions on which concepts can
    perform certain thematic roles
  • Example
  • I wanna eat someplace thats close to campus
  • eat is intransitive and doesnt select an object
    (or theme)
  • You dont want to eat the someplace thats close
    to campus
  • I wanna eat some really good Chinese food today
  • eat is transitive and does select an object (or
    theme) some really good Chinese food

12
Representing selectional restrictions
  • Using event-oriented semantics to capture
    selctional restrictions
  • Hyponomy relations in WordNet Evidence that
    hamburgers are edible

13
Structure of lexemes
  • Primitive decomposition
  • Example (motivated by McCawley (1968)
  • Andrew killed his evil twin
  • Andrew caused his evil twin to become not alive
  • Though kill and cause to become not alive are not
    synonyms, they have the same meaning
  • Decomposing a predicate into a more complex set
    of predicates DO, CAUSE, BECOME, NOT, ALIVE
  • Conceptual Dependency (Schank, 1972) (more
    decomposition) is the most widely used in NLP
  • 11 primitives such as ATRANS (the abstract
    transfer of possession or control from one entity
    to another), PROPEL (the application of physical
    force to move an object)

14
Structure of lexemes
  • Semantic field
  • set of words from a single domain may be captured
    by a more integrated or holistic relationship
    among them
  • The semantic domains that FrameNet employs, such
    as HEALTH CARE, CHANCE, PERCEPTION,
    COMMUNICATION, TRANSACTION, TIME, SPACE, BODY,
    MOTION, etc., can be used to represent a semantic
    field

15
Creativity in the lexicon
  • Metaphor We have in mind a certain concept or
    situation, but we use words and phrases that are
    relevant to totally different kinds of concepts
  • Conventional metaphor (one type)
  • Such as CORPORATION AS PERSON
  • Fuqua Industries, Inc. said Triton Group, Ltd., a
    company it helped resuscitate, has begun
    acquiring Fuqua shares
  • And Ford was hemorrhaging its losses would hit
    1.54 billion in 1980.

16
Creativity in the lexicon
  • Metonymy We denote a concept by using a closely
    related concept
  • Example PLACE FOR INSTITUTION
  • The White House had no comment
  • Example AUTHOR FOR AUTHORS WORKS
  • He likes Shakespeare

17
Computational approaches
  • For metaphor and metonymy
  • Convention-based
  • apply language specific knowledge
  • Reasoning-based
  • not specifically language related but rather a
    general reasoning ability

18
Conclusions
  • Lexical semantics deals with the vast meaning and
    structure of words/lexemes
  • Words cannot be analyzed in isolation
  • They can have multiple meanings, selectional
    restrictions on what can co-occur with them, and
    can be decomposed
  • Databases to help deal with the complexity of
    sense and structure WordNet and FrameNet
  • The lexicon, though a finite list of lexemes, has
    infinite generative power (creativity of
    language)
  • How do we deal with the vastness and creativity
    of language computationally? Decomposition
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