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Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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Page 1: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Metaphors in the (mental)lexicon

Christiane Fellbaum

Princeton University

and

Berlin-Brandenburg Academy of Science

Page 2: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Metaphors: NP,VP

(1) Charley is a tiger.

(2) Pat is a straight arrow.

(3) My job is a jail

(4) Lectures are sleeping pills.

Page 3: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Conventionalized metaphors

X 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)

Page 4: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Metaphors in WordNet

• {tiger, ferocious person,...}

• {straight_arrow, honest person,...}

• *{jail, job,...}

• *{sleeping_pills, lecture,...}

Page 5: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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

Page 6: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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

Page 7: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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

Is-A statements

Page 8: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Class Inclusion?

Glucksberg&Keysar (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

Page 9: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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 arrowsad-hoc metaphors: my job is not a priori

thought of as a jail

Page 10: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Rather than add metaphors as lexical entries in Wordnet...

....Add more similarity-based links to WordNet

Page 11: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

• 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)

Page 12: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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

Page 13: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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

Page 14: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

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.

Page 15: Metaphors in the (mental)lexicon Christiane Fellbaum Princeton University and Berlin-Brandenburg Academy of Science

Prospects

Don’t know the results of the experiments yet

but: many more connections will be created

may teach us about mechanics of metaphor production and comprehension