Word Sense Disambiguation Based on Structured Semantic Space

Word Sense Disambiguation Based on Structured Semantic Space* Ji Donghong Huang Changning Department of Computer Science Tsinghua University Beijing, 100084, P. R. China Email: jdh@s1000e.cs.tsinghua.edu.cn hcn@mail.tsinghua.edu.cn Abstract 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. 1. Introduction Word sense disambiguation has long been one of the major concerns in natural language processing area (e.g., Bruce et al., 1994; Choueka et al., 1985; Gale et al., 1993; McRoy, 1992; Yarowsky 1992, 1994, 1995), whose aim is to identify the correct sense of a word in a particular context, among all of its senses defined in a dictionary or a thesaurus. Undoubtedly, effective disambiguation techniques are of great use in many natural language processing tasks, e.g., machine translation and information retrieving (Allen, 1995; Ng and Lee, 1996; Resnik, 1995), etc. Previous strategies for word sense disambiguation mainly fall into two categories: statistics-based method and exemplar-based method. Statistics-based method often requires large-scale corpora (e.g., Hirst, 1987; Luk, 1995), sense-tagging or not, monolingual or aligned bilingual, as training data to specify significant clues for each word sense. The method generally suffers from the problem of data sparseness. Moreover, huge corpora, especially sense-tagged or aligned ones, are not generally available in all domains for all languages. Exemplar-based method makes use of typical contexts (exemplars) of a word sense, e.g., verb- noun collocations or adjective-noun collocations, and identifies the correct sense of a word in a particular context by comparing the context with the exemplars (Ng and Lee, 1996). Recently, some kinds of learning techniques have been applied to cumulatively acquire exemplars form large corpora (Yarowsky, 1994, 1995). But ideal resources from which to learn exemplars are not generally available for any languages. Moreover, the effectiveness of this method on disambiguating words in large-scale corpora into fine-grained sense distinctions needs to be further investigated (Ng and Lee, 1996). * The work is supported by National Science Foundation of China. 187 A common assumption held by both approaches is that neighboring words provide strong and consistent clues for the correct sense of a target word in some context. In this paper, we also hold the same assumption, but …

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