2012 Presentations (Communicative Events)

Learning the Latent Semantics of a Concept from its Definition

Guo, Weiwei; Diab, Mona

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 construct 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 comparable to state of the art WSD systems.