Paper: Learning the Latent Semantics of a Concept from its Definition

ACL ID P12-2028
Title Learning the Latent Semantics of a Concept from its Definition
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

In this paper we study unsupervised word sense disambiguation (WSD) based on sense definition. We learn low-dimensional latent semantic vectors of concept definitions to con- struct a more robust sense similarity measure wmfvec. Experiments on four all-words WSD data sets show significant improvement over the baseline WSD systems and LDA based similarity measures, achieving results compa- rable to state of the art WSD systems.