A Concept-based Adaptive Approach to Word Sense Disambiguation

A Concept-based Adaptive Approach to Word Sense Disambiguation Jen Nan Chen Jason S. Chang Department of Computer Science Department of Computer Science National Tsing Hua University National Tsing Hua University Hsinchu 30043, Taiwan Hsinchu 30043, Taiwan jnchen@mcu.edu.tw jschang@cs.nthu.edu.tw Abstract Word sense disambiguation for unrestricted text is one of the most difficult tasks in the fields of computational linguistics. The crux of the problem is to discover a model that relates the intended sense of a word with its context. This paper describes a general framework for adaptive conceptual word sense disambiguation. Central to this WSD framework is the sense division and semantic relations based on topical analysis of dictionary sense definitions. The process begins with an initial disambiguation step using an MRD- derived knowledge base. An adaptation step follows to combine the initial knowledge base with knowledge gleaned from the partial disambiguated text. Once the knowledge base is adjusted to suit the text at hand, it is then applied to the text again to finalize the disambiguation result. Definitions and example sentences from LDOCE are employed as training materials for WSD, while passages from the Brown corpus and Wall Street Journal are used for testing. We report on several experiments illustrating effectiveness of the adaptive approach. 1 Introduction Word sense disambiguation for unrestricted text is one of the most difficult tasks in the fields of computational linguistics. The crux of the problem is to discover a model that relates the intended sense of a word with its context. It seems to be very difficult, if not impossible, to statistically acquire enough word-based knowledge about a language necessary to build a robust system capable of automatically disambiguating senses in unrestricted text. For such a system to be effective, a great deal of balanced materials must be assembled in order to cover many idiosyncratic aspects of the language. There exist three issues in a lexicalized statistical word sense disambiguation (WSD) model - data sparseness, lack of a level of abstraction, and static learning strategy. First, word-based models have a plethora of parameters that are difficult to estimate reliably even with a very large corpus. Under-trained models lead to low precision. Second, word- based models lack a degree of abstraction that is crucial for a broad coverage system. Third, a static WSD model is unlikely to be robust and portable, since it is very difficult to make a single static model relevant to a wide variety of unrestricted texts. Recent WSD systems have been developed using word-based model for specific limited domain to disambiguate senses appearing in usually easy context (Leacock, Towell, and Voorlees 1996) with a lot of typical salient words. For unrestricted text, however, the context tends to be very diverse and difficult to capture with a lexicalized model, therefore a corpus-trained system is unlikely to port to new domains and run off the shelf. Generality and adaptiveness are therefore key to a robust and portable WSD system. A concept-based model for WSD requires less parameter and has an element of …

Content truncated to 500 words