Metaphors in the (mental)lexicon

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

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

3. 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)

4. Metaphors in WordNet • {tiger, ferocious person,...} • {straight_arrow, honest person,...} • *{jail, job,...} • *{sleeping_pills, lecture,...}

5. 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

6. 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

7. 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

8. 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

9. 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

10. Rather than add metaphors as lexical entries in Wordnet.......Add more similarity-based links to WordNet

11. 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)

12. 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

13. 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

14. 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.

15. 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