Kernel methods for word sense disambiguation

Many applications of natural language processing (NLP) need an accurate resolution of various ambiguities existing in natural language. The task of fulfilling this need is also called word sense disambiguation (WSD). WSD is to resolve the correct sense for an instance of a polysemous word. On the other hand, as one of the most popular machine learning approaches, kernel methods have attracted significant interest in recent years and have exhibited fairly high performance in a wide variety of learning tasks. In this paper, we present a survey of the research progress of kernel-based WSD techniques. We start by introducing some preliminary knowledge concerning WSD and kernel methods. Then, a review of the main approaches in the literature is presented, focusing on the following issues: context representation, kernel design and learning algorithms. We also provide some further discussions on the kernel-based WSD approaches. Finally, open problems and future directions are discussed.