Supervised Corpus-Based Methods for WSD
Lluı́s Màrquez, Gerard Escudero, David Martínez, Germán Rigau · Kluwer Academic Publishers eBooks · 2006
In this chapter, the supervised approach to word sense disambiguation is presented, which consists of automatically inducing classification models or rules from annotated examples. We start by introducing the machine learning framework for classification and some important related concepts. Then, a review of the main approaches in the literature is presented, focusing on the following issues: learning paradigms, corpora used, sense repositories, and feature representation. We also include a more detailed description of five statistical and machine learning algorithms, which are experimentally evaluated and compared on the DSO corpus. In the final part of the chapter, the current challenges of the supervised learning approach to WSD are briefly discussed.