Generalizing semantic relations

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. Problem • Many different ways to express equivalent meaning • Consider resides relation • Table shows counts of reverb relations containing liveor reside • Relations in red should be generalized to: • reside(<PERSON>,<PLACE>) • 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

6. Semantic Clustering Goals • Semantic Relations Dictionary • Mapping from ReVerb’s specific instances to generalized semantic-placeholder that looks like <Generalized-Rel>(<Arg1 Type>,<Arg2 Type>) • 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

7. 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

8. Ontological Similarity (Clustering) • Mattresides in Sendai • Ericlives in Japan • Should these be clustered together? (Yes!) • Matching arg1 type <Person> • Matching arg2 type <Place> • High ontological similarity means good chance of clustering

9. Ontological Similarity (Lookup) • Mattlives on a farm => ??? • Ericlives on donuts => ??? • Are these the same semantic relation? (NO!) • Multiple entries in dictionary for lives_on: • resides(<Living Thing>,<Place>) • nourished_by(<Living Thing>,<Nourishment>) • Use argument type similarity testing to differentiate between senses of lives_on

10. Ontological Similarity (Lookup cont.) • Which version will ontological similarity suggest we return for each example? • Mattlives on a farm <Person>lives on<?> <Place> resides(Matt,a_farm) • Ericlives on donuts <Person>lives on<?> <Food> nourished_by(Eric,donuts) • onto_sim(<Food>,<Nourishment>) is greater than onto_sim(<Food>,<Place>) so we know knows Eric is nourished_bydonuts

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

12. 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

13. 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?

14. Other Issues • Word tense • Does lived in belong with lives in? • Detection of conflicting polarity  • (Acesulfame_Potassiumdoes_not_promotetooth_decay) • (Conservatives should_not_promote democracy)  • (Website must_not_promote hate)  • ? (Environmentalists are_not_alone_in_promotingrenewable_energy) • Semantic type coverage problems • Use lexical similarity-based lookup for semantic type too?

15. 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

16. 計画！！！！！ • Finish similarity score • Selecting a wordnetontological 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