CS 4705

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• Relations Between Words

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Today

• Word Clustering
• Words and Meaning
• Lexical Relations
• WordNet
• Clustering for word sense discovery

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Related Words Clustering

• Clustering feature vectors to discover word
  senses using some similarity metric (e.g. cosine
  distance)
• Represent each cluster as average of feature
  vectors it contains
• Label clusters by hand with known senses
• Classify unseen instances by proximity to these
  known and labeled clusters
• Evaluation problem
• What are the right senses?

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• Cluster impurity
• How do you know how many clusters to create?
• Some clusters may not map to known senses

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Related Words Dictionary Entries

• 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

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• 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

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Homonymy

• 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 clients name.
• As agriculture is burgeoning on the east bank,
  the river will shrink even more
• Word sense disambiguation what clues?
• Related phenomena
• homophones - read and red (same pron/different
  orth)
• homographs - bass and bass (same orth/different
  pron)

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Ambiguity Which applications will these cause
problems for?

• A bass, the bank, red/read
• General semantic interpretation
• Machine translation
• Spelling correction
• Speech recognition
• Text to speech
• Information retrieval

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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.
• Whats the difference between polysemy and
  homonymy?
• Homonymy
• Distinct, unrelated meanings
• Different etymology? Coincidental similarity?

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• 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?

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Tropes, or Figures of Speech

• Metaphor one entity is given the attributes of
  another (tenor/vehicle/ground)
• Life is a bowl of cherries. Dont 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. (deferred
  reference)
• Both extend existing sense to new meaning
• Metaphor completely different concept
• Metonymy related concepts

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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 Hes really cheap/?parsimonious.
• collocational constraints
• roast beef, ?baked beef
• economy fare ?economy price

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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 dont appear
  in some specific similar contexts
• Flu victims, flu suffers,
• Crime victims, ?crime sufferers,

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Hyponomy

• General hypernym (superordinate)
• 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)

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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

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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

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• 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)

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Next time

• Chapter 18.10