Paper: A Structured Vector Space Model for Word Meaning in Context

ACL ID D08-1094
Title A Structured Vector Space Model for Word Meaning in Context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We address the task of computing vector space representations for the meaning of word oc- currences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing mod- els for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorpo- rating the selectional preferences for words’ argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model per- forms at and above the state of the art for mod- eling the contextual adequacy of paraphrases.