Paper: Contextualizing Semantic Representations Using Syntactically Enriched Vector Models

ACL ID P10-1097
Title Contextualizing Semantic Representations Using Syntactically Enriched Vector Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

We present a syntactically enriched vec- tor model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It em- ploys a systematic combination of first- and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previ- ous work on a paraphrase ranking task, and (ii) achieves promising results on a word- sense similarity task; to our knowledge, it is the first time that an unsupervised method has been applied to this task.