TY - GEN
T1 - A two level model for context sensitive inference rules
AU - Melamud, Oren
AU - Berant, Jonathan
AU - Dagan, Ido
AU - Goldberger, Jacob
AU - Szpektor, Idan
PY - 2013
Y1 - 2013
N2 - Automatic acquisition of inference rules for predicates has been commonly addressed by computing distributional similarity between vectors of argument words, operating at the word space level. A recent line of work, which addresses context sensitivity of rules, represented contexts in a latent topic space and computed similarity over topic vectors. We propose a novel two-level model, which computes similarities between word-level vectors that are biased by topic-level context representations. Evaluations on a naturallydistributed dataset show that our model significantly outperforms prior word-level and topic-level models. We also release a first context-sensitive inference rule set.
AB - Automatic acquisition of inference rules for predicates has been commonly addressed by computing distributional similarity between vectors of argument words, operating at the word space level. A recent line of work, which addresses context sensitivity of rules, represented contexts in a latent topic space and computed similarity over topic vectors. We propose a novel two-level model, which computes similarities between word-level vectors that are biased by topic-level context representations. Evaluations on a naturallydistributed dataset show that our model significantly outperforms prior word-level and topic-level models. We also release a first context-sensitive inference rule set.
UR - http://www.scopus.com/inward/record.url?scp=84906931955&partnerID=8YFLogxK
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AN - SCOPUS:84906931955
SN - 9781937284503
T3 - ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
SP - 1331
EP - 1340
BT - Long Papers
PB - Association for Computational Linguistics (ACL)
T2 - 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Y2 - 4 August 2013 through 9 August 2013
ER -