Sense clusters versus sense relations

1. Sense clusters versus sense relations Irina Chugur, Julio Gonzalo UNED (Spain)

2. Sense clusters vs. Sense relations • Arguments for sense clustering • subtle distinctions produce noise in applications • WN too fine-grained • Remove predictable sense extensions?

3. Sense clusters vs. Sense relations • Arguments for sense clustering • subtle distinctions produce noise in applications • WN too fine-grained • Remove predictable sense extensions? But... clusters are not absolute (e.g. metaphors in IR/MT) not really! Use them to infer and study systematic polysemy Polysemy relations are more informative and predictive WN rich sense distinctions permit empirical /quantitative studies on polysemy phenomena

4. Sense clusters vs. Sense relations • Arguments for sense clustering • subtle distinctions produce noise in applications • WN too fine-grained • Remove predictable sense extensions? But... clusters are not absolute (e.g., are metaphors close?) not really! Use them to infer and study systematic polysemy Polysemy relations are more informative and predictive Annotation of semantic relations in 1000 wn nouns

5. 1) Cluster evidence from Semcor • Hypothesis: if two senses tend to co-occur in the same documents, they are not good IR discriminators. • Criterion: cluster senses that co-occur frequently in IR-Semcor collection. • Example: fact 1 and fact 2 co-occur in 13 out of 171 docs. • Fact 1. (a piece of information about circumstances that exist or events that have occurred) • Fact 2. (a statement or assertion of verified information about something that is the case or has happened)

6. English Spanish Band 1 Instrumentalists not including string players Band 2 A group of musicians playing popular music for dancing Orquesta 4 Orquesta 4 2) Cluster evidence from parallel polysemy French German Band 2 Groupe 9 Groupe 6 Band 2

7. Parallel polysemy in EuroWordNet English Spanish French German {child,kid}  {niño,crío,menor}{enfant,mineur}  {Kind} {male child,  {niño}  {enfant}  {Kind,Spross} Boy,child}

8. Comparison of clustering criteria

9. Clusters vs. semantic relations Polysemy relations are more predictive!

10. Characterization of sense inventories for WSD • Given two senses of a word, • How are they related? (polysemy relations) • How closely? (sense proximity) • In what applications should be distinguished? • Given an individual sense of a word • Should it be split into subsenses? (sense stability)

11. Cross-Linguistic evidence Fine 40129 Mountains on the other side of the valley rose from the mist like islands, and here and there flecks of cloud as pale and <tag>fine</tag> as sea-spray, trailed across their sombre, wooded slopes. TRANSLATION: * *

12. 1  trL(x) = trL(y) |wi|| wj| x {wi examples } y {wj examples } 1  PL(same lexicalization|wi, wj) L languages |languages| Sense proximity (Resnik & Yarowsky) PL(same lexicalization|wi, wj)  Proximity(wi, wj) 

13. 182 senses 508 examples Experiment Design MAIN SET 44 Senseval-2 words (nouns and adjectives) Bulgarian Russian Spanish Urdu 11 native/bilingual speakers of 4 languages (control set: 12 languages, 5 families, 28 subjects)

14. RESULTS: distribution of proximity indexes Average proximity = 0.29: same as Hector in Senseval 1!

15. distribution of homonyms ?

16. distribution of metaphors

17. distribution of metonymy Average proximity: target in source0.64, source in target0.37

18. Annotation of 1000 wn nouns Need for cluster here!

19. Typology of sense relations Homonymy Metonymy Metaphor Specialization Generalization Equivalence fuzzy

20. Typology of sense relations: metonymy Animal-meat Animal-fur Tree-wood Object-color Plant-fruit People-language Action-duration Recipient-quantity ... Homonymy Metonymy Metaphor Specialization Generalization Equivalence fuzzy target in source source in target Co-metonymy Action-object Action-result Shape-object Plant-food/beverage Material-product ... Substance-agent

21. Action/state/entity (source domain) Action/state/entity (target domain) Typology of sense relations: metaphors Homonymy Metonymy Metaphor (182) Specialization Generalization Equivalence fuzzy object  object / person (47) person  person (21) physical action  abstract action (16) Physical property  abstract property (11) Animal  person (10) ...

22. Action/state/entity (source domain) Action/state/entity (target domain) Typology of sense relations: metaphors Homonymy Metonymy Metaphor (182) Specialization Generalization Equivalence fuzzy object  object / person (47) person  person (21) • Source: • historical, mythological, biblical character... • profession, occupation, position ... • Target: prototype person • e.g. Adonis (greek mythology/handsome)

23. Conclusions Let’s annotate semantic relations between WN word senses!