Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity

Mohammad Taher Pilehvar, David Jurgens and Roberto Navigli

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

Semantic similarity is an essential component of many Natural Language Processing applications. However, prior methods for computing semantic similarity often operate at different levels, e.g., single words or entire documents, which requires adapting the method for each data type. We present a unified approach to semantic similarity that operates at multiple levels, all the way from comparing word senses to comparing text documents. Our method leverages a common probabilistic representation over word senses in order to compare different types of linguistic data. This unified representation shows state-of-the-art performance on three tasks: semantic textual similarity, word similarity, and word sense coarsening.

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