Word Sense Disambiguation Based on Structured Semantic Space

In this paper, we propose a framework, structured semantic space, as a foundation for word sense disarnbiguation tasks, and present a strategy to identify the correct sense of a word in some context based on the space. The semantic space is a set of multidimensional real-valued vectors, which formally describe the contexts of words. Instead of locating all word senses in the space, we only make use of mono-sense words to outline it. We design a merging procedure to establish the dendrogram structure of the space and give an heuristic algorithm to find the nodes (sense clusters) corresponding with sets of similar senses in the dendrogram. Given a word in a particular context' the context would activate some clusters in the dendrogram, based on its similarity with the contexts of the words in the clusters, then the correct sense of the word could be determined by comparing its definitions with those of the words in the clusters.