Paper: Measuring Distributional Similarity in Context

ACL ID D10-1113
Title Measuring Distributional Similarity in Context
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010

The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and para- phrases to word sense disambiguation and tex- tual entailment. Vector-based models are typ- ically directed at representing words in isola- tion and thus best suited for measuring simi- larity out of context. In his paper we propose a probabilistic framework for measuring sim- ilarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of la- tent senses and is modulated by context. Ex- perimental results on lexical substitution and word similarity show that our algorithm out- performs previously proposed models.