Metaphors in the (mental)lexicon

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Metaphors in the (mental)lexicon

• Christiane Fellbaum
• Princeton University
• and
• Berlin-Brandenburg Academy of Science

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Metaphors NP,VP

• Charley is a tiger.
• (2) Pat is a straight arrow.
• (3) My job is a jail
• (4) Lectures are sleeping pills.

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Conventionalized metaphorsX is a tigerY is a
straight arrow

• Tiger, straight arrow are lexicalized
• Case of polysemy
• Can be integrated into WordNet and disambiguated
  fairly easily (Fellbaum, 98)

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Metaphors in WordNet

• tiger, ferocious person,...
• straight_arrow, honest person,...
• jail, job,...
• sleeping_pills, lecture,...

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Ad-hoc metaphors

• Jail, sleeping pills are not lexicalized
• Should not appear in dictionaries
• Are created on the fly
• There are infinitely many
• Based on similarity between vehicle (jail) and
  topic (job) in terms of salient features

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What to do about ad-hoc metaphors in the
lexicon/WordNet

• Metaphors are based on similarity
• Similarity is based on shared features
• Hyponymy captures only some features
• This accounts for ad-hoc superordinates and
  metaphors

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Is-A statements

• ISA-statements are ambiguous between class
  inclusion and identity
• A car is an automobile (identity/synonymy)
• A Ford is a car (class inclusion)
• Only identity statements can be reversed
• An automobile is a car
• A car is a Ford

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Class Inclusion?

• GlucksbergKeysar (1990) metaphors are class
  inclusion statements
• Attribution to an ad-hoc superordinate of which
  vehicle is a prototypical member
• jail1 prison
• jail2 confinement, lack of freedom,...
• jail2 is prototypical member of category jail2

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Similarity

• Metaphor establishes new similarity based on
  shared features specific to a context
• conventionalized metaphors
• Charley, Pat are not a priori thought of as
  tigers or straight arrows
• ad-hoc metaphors my job is not a priori thought
  of as a jail

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Rather than add metaphors as lexical entries in
Wordnet.......Add more similarity-based links to
WordNet
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• Add weighted arcs between all synsets
• First step human annotators rate strength with
  which one concept evokes another concept
• Second step extrapolate remaining arcs from
  manually rated associations
• Feature vectors
• WordNet relations, other lexical info (POS)
• Indirect co-occurrence
• (Work in progress Fellbaum, Osherson, Schapire,
  Charikar, Basu, Predd, Hauser)

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Enriched WordNet can account for contextualized
similarity

• My dorm is a jail (similarity based on context
  physical location)
• My job is a jail (similarity based on context
  freedom/autonomy)
• We represent human judgment of similarity by a
  function s, such that s(C, s1, s2) measures the
  similarity of s1 and s2 along the dimension
  picked out by C
• C context
• s1 synset1
• s2 synset2

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Example

• C freedom, liberty,...
• s1 job, place_of_work,...
• s2 jail, prison,...
• We want to measure the similarity of job and jail
  in the context of freedom

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Defining the function

• Use concept of path length (distribution of path
  lengths connecting s1 and s2)
• A rich set of short paths (plus WN-style
  similarity) indicates high similarity
• Length of path must be modulated by C!
• Consider path from s1 to s2 via synset X
• If X and C are connected via a short path, then
  s1 and s2 are similar.
• If X and C are connected via a long path, then X
  is irrelevant to C, and length s1-X-s2 is
  increased, making s1 and s2 less similar.

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Prospects

• Dont know the results of the experiments yet
• but many more connections will be created
• may teach us about mechanics of metaphor
  production and comprehension