Paper: Probabilistic models of similarity in syntactic context

ACL ID D11-1097
Title Probabilistic models of similarity in syntactic context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

This paper investigates novel methods for in- corporating syntactic information in proba- bilistic latent variable models of lexical choice and contextual similarity. The resulting mod- els capture the effects of context on the inter- pretation of a word and in particular its effect on the appropriateness of replacing that word with a potentially related one. Evaluating our techniques on two datasets, we report perfor- mance above the prior state of the art for esti- mating sentence similarity and ranking lexical substitutes.