PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Measuring Distributional Similarity in Context
Georgiana Dinu and Mirella Lapata
In: EMNLP 2010, 9-11 Oct 2010, Cambridge, MA, USA.

This is the latest version of this eprint.

Abstract

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 paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:8156
Deposited By:Georgiana Dinu
Deposited On:08 June 2011

Available Versions of this Item