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
Authors

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.