An inference-based model of word meaning in context as a paraphrase distribution

Authors:
Taesun Moon;Katrin Erk
Affiliations:
The University of Texas at Austin, Austin, TX;The University of Texas at Austin, Austin, TX
Venue:
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Year:
2013

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Abstract

Graded models of word meaning in context characterize the meaning of individual usages (occurrences) without reference to dictionary senses. We introduce a novel approach that frames the task of computing word meaning in context as a probabilistic inference problem. The model represents the meaning of a word as a probability distribution over potential paraphrases, inferred using an undirected graphical model. Evaluated on paraphrasing tasks, the model achieves state-of-the-art performance.