1. GENERALIZING SEMANTIC RELATIONS １２月７日 研究会 祭都援炉 ( マットエンロ )

2. Up until now: Getting to know NLP “Speech and Language Processing” (Jurafsky & Martin) 論文： On-Demand Information Extract (Sekine) Learning First-Order Horn Clauses From Web Text [Sherlock] (Schoenmackers 2010) Coupled Semi-Supervised Learning for Information Extraction [NELL] (Carlson) Identifying Relations for Open Information Extraction [ReVerb] (Fader) Relation Acquisition using Word Classes and Partial Patterns (Saeger) Interpretation as Abduction (Hobbs) An ILP Formulation of Abudctive Inference for Discourse Interpretation (Inoue 氏 ) Learning Dependency-Based Compositional Semantics (Liang)

3. Motivation Ultimate Goal: Inference Inference requires: knowledge Large scale database of semantic relations have been created from web text

4. ReVerb (Fader et al., 2011) relation(arg1,arg2) tuples acquired from large-scale Web data Over 14.5 million semantic relations released to public

5. 5 ReVerb - Predicate Detection POS tag patterns: V W* P Milk is a rich source of calcium which is critical for building strong bones. ARG1RELATIONARG2 (is a rich source of) (is critical for) Slide stolen from Eric Nichol’s kenkyuukai 2011-11-02

6. 6 ReVerb - Argument Detection Noun Phrase chunking Milk is a rich source of calcium which is critical for building strong bones. ARG1RELATION ARG2 (Milk is a rich source of calcium) (calcium is critical for strong bones) Slide modified from Eric Nichol’s kenkyuukai 2011-11-02

7. Examples (from Fader 2011)

8. Problem Many different ways to express equivalent meaning Consider resides relation Table shows counts of reverb relations containing live or reside Relations in red should be generalized to: reside(, ) We aim to generalize through semantic clustering Frequency | Relation 27,383 lives in 10,315 live in 8,653 lived in 5,185 currently resides in 4,002 currently lives in 3,310 now lives in 1,933 resides in 1,548 is a resident of 1,468 live on 1,308 now resides in 1,191 has lived in 1,055 resided in 876 lives on 590 lived on 531 live at 515 still lives in 461 can live up to 456 is a lifelong resident of 444 was a resident of 413 live for 382 must be residents of 332 lives with 332 lived for

9. Semantic Clustering Store generalizations in the form (, ) Within a semantic relation generalization dictionary

10. Semantic Clustering Goals 1. Semantic Relations Dictionary Mapping from ReVerb’s specific instances to generalized semantic- placeholder that looks like (, ) 2. Method of mapping real-world relation instances to generalized semantic form Can be accomplished with a semantic similarity function Clustering and generalize relations Looking up new relations from text

11. Semantic Similarity Ontological: Similarity based on arguments’ hierarchy of semantic types Lexical: Similarity based on lexical features of relation Contextual: Similarity based on surrounding text

12. Ontological Similarity Determine type of arg1 and arg2 WordNet synset Sherlock semantic class (Schoenmackers 2010) Use WordNet similarity functions on argument type

13. Ontological Similarity (Clustering) 1. Matt resides in Sendai 2. Eric lives in Japan Should these be clustered together? (Yes!) Matching arg1 type Matching arg2 type High ontological similarity means good chance of clustering

14. Ontological Similarity (Lookup) 1. Matt lives on a farm => ??? 2. Eric lives on donuts => ??? Are these the same semantic relation? (NO!) Multiple entries in dictionary for lives_on: resides(, ) nourished_by(, ) Use argument type similarity testing to differentiate between senses of lives_on

15. Ontological Similarity (Lookup) 1. Matt lives on a farm => resides (Matt,a_farm) 2. Eric lives on donuts => ??? Are these the same semantic relation? (NO!) Multiple entries in dictionary for lives_on: resides(, ) nourished_by(, ) Use argument type similarity testing to differentiate between senses of lives_on

16. Ontological Similarity (Lookup) 1. Matt lives on a farm => resides(Matt,a_farm) 2. Eric lives on donuts => nourished_by(Eric,donuts) Are these the same semantic relation? (NO!) Multiple entries in dictionary for lives_on: resides(, ) nourished_by(, ) Use argument type similarity testing to differentiate between senses of lives_on

17. Ontological Similarity (Lookup cont.) Which version will ontological similarity suggest we return for each example? 1. Matt lives on a farm lives on resides(Matt,a_farm) 2. Eric lives on donuts lives on nourished_by(Eric,donuts) onto_sim(, ) is greater than onto_sim(, ) so we know knows Eric is nourished_by donuts

18. Lexical Similarity Use relationship features to score similarity N-gram overlap, bag-of-words, … Weighting content/functional words differently etc

19. Lexical Similarity Correctly groups together Lives at Live in But erroneously clusters Lives for Lives with And doesn’t cluster resides in (relying on ontological sim. for that) 27,383 lives in 10,315 live in 8,653 lived in 5,185 currently resides in 4,002 currently lives in 3,310 now lives in 1,933 resides in 1,548 is a resident of 1,468 live on 1,308 now resides in 1,191 has lived in 1,055 resided in 876 lives on 590 lived on 531 live at 515 still lives in 461 can live up to 456 is a lifelong resident of 444 was a resident of 413 live for 382 must be residents of 332 lives with 332 lived for

20. Contextual Similarity How similar is the surrounding text? To answer this, we need original text Will have to hunt down sentences on the web Time consuming Feasible?

21. Issues - Clustering Huge data-set O(n^2) clustering algorithms are infeasible Investigating efficient methods: Hierarchical Clustering Co-Clustering (Dhillon et al., 2003) Probablistic Latent Semantic Indexing Location Sensitive Hashing

22. Other Issues Word tense Does lived in belong with lives in? Detection of conflicting polarity x (Acesulfame_Potassium does_not_promote tooth_decay) x (Conservatives should_not_promote democracy) x (Website must_not_promote hate) x ? (Environmentalists are_not_alone_in_promoting renewable_en ergy) Semantic type coverage problems Use lexical similarity-based lookup for semantic type too?

23. PROGRESS 1. Up to now 2. Looking ahead

24. 進捗報告 Set up git repository Implemented: Wrapper for reverb (data lookup) WordNet type-lookup Sherlock type-lookup Ontological similarity Made a slideshow for 研究会 ただ今ご覧になっていた だいている物

25. 計画！！！！！ Finish similarity score Selecting a wordnet ontological similarity function (Over 5 different evaluations already exist) Implement lexical similarity (Should already be in NLTK somewhere) Implementing contextual similarity (Prepare for the hunt!) Selecting & implementing a clustering method Test on ReVerb data First on wikipedia… Then on clueweb