Hiram Calvo and Alexander Gelbukh Presented by Igor A. Bolshakov

1. Center for Computing ResearchNational Polytechnic InstituteMexico Acquiring Selectional Preferences from Untagged Text for Prepositional Phrase Attachment Disambiguation Hiram Calvo and Alexander Gelbukh Presented by Igor A. Bolshakov

2. Introduction • Entities must be identified adequately for database representation: • See the cat with a telescope • See [the cat] [with a telescope] 2 entities • See [the cat with a telescope]1 entity • Problem is known as Prepositional Phrase (PP) attachment disambiguation.

3. Existing methods - 1 • Accuracy when using treebank statistics: • Ratnaparkhi et al.,Brill and Resnik: up to 84% • Kudo and Matsumoto: 95.8% • Needed weeks for training • Lüdtke and Sato: 94.9% • Only 3 hours for training • But there are no treebanks for many languages!

4. Existing methods - 2 • Based on Untagged text: • Calvo and Gelbukh, 2003: 82.3% accuracy • Uses the web as corpus: • Slow (up to 18 queries for each PP attachment ambiguity) • Does this method work with very big local corpora?

5. Using a big local corpus • Corpus • 3 years of publication of 4 newspapers • 161 million words • 61 million sentences • Results: • Recall: 36% Precision: 67% • Dissapointing!

6. What do we want? • To solve PP attachment disambiguation with • Local corpora, not web • No treebanks • No supervision • High precision and recall • Solution proposed: • Selectional Preferences

7. Selectional Preferences • The problem of I see a cat with a telescope turns into I see {animal} with {instrument}

8. Sources for noun semantic classification • Machine-Readable dictionaries • WordNet ontology • We use the top 25 unique beginner concepts of WordNet • Examples: mouse is-a {animal}, ranch is-a {place}, root is-a part}, reality is-a {atrtibute}, race is-a {grouping}, etc.

9. Extracting Selectional Preferences • Text is shallow parsed • Subordinate sentences are separated • Patterns are searched 1. Verb NEAR Preposition NEXT_TO Noun 2. Verb NEAR Noun 3. Noun NEAR Verb 4. Noun NEXT_TO Preposition NEXT_TO Noun • All Nouns are classified

10. Example • Consider this toy-corpus: • I see a cat with a telescope • I see a ship in the sea with a spyglass The following patterns are extracted: • See,cat see,{animal} • See,with,telescope see,with,{instrument} • Cat,with,telescope {animal},with,{instrument} • See,ship see,{thing} • See,in,sea see,in,{place} • See,with,spyglass see,with,{instrument} • Ship,in,sea {thing},in,{place}

11. Example • See, with, {instrument} has two occurrences • {Animal}, with, {instrument} has one occurrence • Thus, • See with {instrument} is more probable than {animal} with {instrument}

12. Experiment • Now, with a real corpus, we apply the following formula: • X can be a specific verb or a noun’s semantic class (see or {animal}) • P is a preposition (with) • C2 is the class of the second noun {instrument}

13. Experiment • From the corpus of 161 million words of Spanish Mexican newspaper the system obtained: • 893,278 selectional preferences for 5,387verbs, and • 55,469noun patterns (like {animal} with {instrument})

14. Evaluation • We tested the obtained Selectional Preferences doing PP attachment disambiguation on 546 sentences from the LEXESP corpus (in Spanish). • Then we compared manually with the correct PP attachments. • Results: precision 78.2%, recall: 76.0%

15. Conclusions • Results not as good as those obtained by other methods (up to 95%) • But we don’t need any costly resources, such as: • Treebanks • Manually anotated corpora • Web as corpus

16. Future Work • To use not only 25 fixed semantic classes (top concepts) but the whole hierarchy • To use a WSD module • Currently if a word belongs to more than one class, all classes are taken into accoutb

17. Thank you! hiram@sagitario.cic.ipn.mx gelbukh@cic.ipn.mx