Supervised word sense disambiguation using semantic diffusion kernel

The success of machine learning approaches to word sense disambiguation (WSD) is largely dependent on the representation of the context in which an ambiguous word occurs. Typically, the contexts are represented as the vector space using “Bag of Words (BoW)” technique. Despite its ease of use, BoW representation suffers from well-known limitations, mostly due to its inability to exploit semantic similarity between terms. In this paper, we apply the semantic diffusion kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information, to smooth the BoW representation for WSD systems. Semantic diffusion kernel can be obtained through a matrix exponentiation transformation on the given kernel matrix, and virtually exploits higher order co-occurrences to infer semantic similarity between terms. The superiority of the proposed method is demonstrated experimentally with several SensEval disambiguation tasks.