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					Lexical Semantics




      CS 4705
                        Today

•   Words and Meaning
•   Lexical Relations
•   WordNet
•   Thematic Roles
•   Selectional Restrictions
•   Conceptual Dependency
           Thinking about Words Again

• Lexeme: an entry in the lexicon that includes
   – an orthographic representation
   – a phonological form
   – a symbolic meaning representation or sense
• Some typical dictionary entries:
   – Red (‘red) n: the color of blood or a ruby
   – Blood (‘bluhd) n: the red liquid that circulates in the
     heart, arteries and veins of animals
   – Right (‘rIt) adj: located nearer the right hand esp. being
     on the right when facing the same direction as the
     observer
   – Left (‘left) adj: located nearer to this side of the body
     than the right
• Can we get semantics directly from online
  dictionary entries?
   – Some are circular
   – All are defined in terms of other lexemes
   – You have to know something to learn something
• What can we learn from dictionaries?
   – Relations between words:
      • Oppositions, similarities, hierarchies
                     Homonomy

• Homonyms: Words with same form – orthography
  and pronunciation -- but different, unrelated
  meanings, or senses (multiple lexemes)
  – A bank holds investments in a custodial account in the
    client’s name.
  – As agriculture is burgeoning on the east bank, the river
    will shrink even more
• Word sense disambiguation: what clues?
• Similar phenomena
  – homophones - read and red (same pron/different orth)
  – homographs - bass and bass (same orth/different pron)
  Ambiguity: Which applications will these
           cause problems for?
A bass, the bank, /red/
• General semantic interpretation
• Machine translation
• Spelling correction
• Speech recognition
• Text to speech
• Information retrieval
                       Polysemy

• Word with multiple but related meanings (same
  lexeme)
   – They rarely serve red meat.
   – He served as U.S. ambassador.
   – He might have served his time in prison.
• What’s the difference between polysemy and
  homonymy?
• Homonymy:
   – Distinct, unrelated meanings
   – Different etymology? Coincidental similarity?
• Polysemy:
   – Distinct but related meanings
   – idea bank, sperm bank, blood bank, bank bank
   – How different?
       • Different subcategorization frames?
       • Domain specificity?
       • Can the two candidate senses be conjoined?
       ?He served his time and as ambassador to Norway.
• For either, practical task:
   – What are its senses? (related or not)
   – How are they related? (polysemy ‘easier’ here)
   – How can we distinguish them?
            Tropes, or Figures of Speech

• Metaphor: one entity is given the attributes of another
  (tenor/vehicle/ground)
   – Life is a bowl of cherries. Don’t take it serious….
   – We are the eyelids of defeated caves. ??
• Metonymy: one entity used to stand for another (replacive)
   – GM killed the Fiero.
   – The ham sandwich wants his check.
• Both extend existing sense to new meaning
   – Metaphor: completely different concept
   – Metonymy: related concepts
                      Synonymy

• Substitutability: different lexemes, same meaning
   – How big is that plane?
   – How large is that plane?
   – How big are you? Big brother is watching.
• What influences substitutability?
   – Polysemy (large vs. old sense)
   – register: He’s really cheap/?parsimonious.
   – collocational constraints:
       roast beef, ?baked beef
       economy fare ?economy price
  Finding Synonyms and Collations Automatically
                 from a Corpus

• Synonyms: Identify words appearing frequently in
  similar contexts
   Blast victims were helped by civic-minded passersby.
   Few passersby came to the aid of this crime victim.
• Collocations: Identify synonyms that don’t appear
  in some specific similar contexts
   Flu victims, flu suffers,…
   Crime victims, ?crime sufferers, …
                      Hyponomy
• General: hypernym (super…ordinate)
   – dog is a hypernym of poodle
• Specific: hyponym (under..neath)
   – poodle is a hyponym of dog
• Test: That is a poodle implies that is a dog
• Ontology: set of domain objects
• Taxonomy? Specification of relations between
  those objects
• Object hierarchy? Structured hierarchy that
  supports feature inheritance (e.g. poodle inherits
  some properties of dog)
                 Semantic Networks
• Used to represent lexical relationships
   – e.g. WordNet (George Miller et al)
   – Most widely used hierarchically organized lexical
     database for English
   – Synset: set of synonyms, a dictionary-style definition
     (or gloss), and some examples of uses --> a concept
   – Databases for nouns, verbs, and modifiers
• Applications can traverse network to find
  synonyms, antonyms, hierarchies,...
   – Available for download or online use
   – http://www.cogsci.princeton.edu/~wn
    Using WN, e.g. in Question-Answering
• Pasca & Harabagiu ’01 results on TREC corpus
   – Parses questions to determine question type, key words (Who
     invented the light bulb?)
   – Person question; invent, light, bulb
   – The modern world is an electrified world. It might be argued
     that any of a number of electrical appliances deserves a
     place on a list of the millennium's most significant inventions.
     The light bulb, in particular, profoundly changed human
     existence by illuminating the night and making it hospitable
     to a wide range of human activity. The electric light, one of
     the everyday conveniences that most affects our lives, was
     invented in 1879 simultaneously by Thomas Alva Edison in
     the United States and Sir Joseph Wilson Swan in England.
• Finding named entities is not enough
• Compare expected answer ‘type’ to potential
  answers
   – For questions of type person, expect answer is person
   – Identify potential person names in passages retrieved by
     IR
   – Check in WN to find which of these are hyponyms of
     person
• Or, Consider reformulations of question: Who
  invented the light bulb
   – For key words in query, look for WN synonyms
   – E.g. Who fabricated the light bulb?
   – Use this query for initial IR
• Results: improve system accuracy by 147% (on
  some question types)
                    Thematic Roles
• E w,x,y,z {Giving(x) ^ Giver(w,x) ^ Givee(z, x) ^
  Given(y,x)}
• A set of roles for each event:
   – Agent: volitional causer -- John hit Bill.
   – Experiencer: experiencer of event – Bill got a
     headache.
   – Force: non-volitional causer – The concrete block
     struck Bill on the head.
   – Theme/patient: most affected participant – John hit Bill.
   – Result: end product – Bill got a headache.
   – Content: proposition of propositional event – Bill
     thought he should take up martial arts.
   – Instrument: instrument used -- John hit Bill with a bat
   – Beneficiary: qui bono – John hit Bill to avenge his
     friend
   – Source: origin of object of transfer event – Bill fled
     from New York to Timbuktu
   – Goal: destination of object -- Bill led from New York to
     Timbuktu
• But there are a lot of verbs, with a lot of
  frames…
• Framenet encoded frames for many verb
  categories
 Thematic Roles and Selectional Restrictions

• Selectional restrictions: semantic constraint that a
  word (lexeme) imposes on the concepts that go
  with it
George hit Bill with
             ….John/a gun/gusto.
Jim killed his philodendron/a fly/Bill.
?His philodenron killed Jim.
The flu/Misery killed Jim.
    Thematic Roles/Selectional Restrictions

•   In practical use:
    – Given e.g. a verb and a corpus (plus FrameNet)
    – What conceptual roles are likely to accompany it?
    – What lexemes are likely to fill those roles?
    Assassinate
    Give
    Imagine
    Fall
    Serve
       Schank's Conceptual Dependency

• Eleven predicate primitives represent all
  predicates
• Objects decomposed into primitive categories and
  modifiers
• But few predicates result in very complex
  representations of simple things
   Ex,y Atrans(x) ^ Actor(x,John) ^ Object(x,Book) ^
     To(x,Mary) ^ Ptrans(y) ^ Actor(y,John) ^
     Object(y,Book) ^ To(y,Mary)
   John caused Mary to die vs. John killed Mary
                   Next time

• Some word relations and how we might identify
  them
• Chapter 18.6-9

				
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