Semantic Unsupervised Learning for Word Sense Disambiguation

Abstract

The identification of the particular meaning for a word based on the context of its usage is commonly referred to as Word Sense Disambiguation (or WSD). Although considered a complex task, WSD is an important component of language processing and information analysis systems in several fields. Current methods for WSD rely on human input and are usually limited to a finite set of words. Complicating matters further, language is dynamic with (current) word usage changes and the introduction of new words. Static definitions created by previously defined analyses become outdated or are inadequate to deal with current usage. Fully automated methods for WSD are needed both for sense discovery and for distinguishing the sense being used for a word in context. Latent Semantic Analysis (LSA) is a candidate automated unsupervised learning system that has not been widely applied in this area. In this chapter, advanced LSA techniques are deployed as an unsupervised learning approach to the WSD tasks of sense discovery and distinguishing senses in use.