Abstract
This paper describes a heuristic approach to automatic acquisition of contextual representations of senses in a machine-readable dictionary (MRD). Including contextual information in an MRD-based lexical database offers several benefits. First, the representation can be used to merge closely related senses and construct a coarser sense division, so unnecessarily fine sense distinctions can be avoided in word sense disambiguation (WSD). The contextual information can also be used as a knowledge base to develop a WSD system. Furthermore, if the algorithms run on several MRDs, the contextual representation also provides a means of linking relevant senses across multiple MRDs to create an integrated lexical database. The algorithms are based primarily on information retrieval techniques to build a list of topics that are most relevant to the definition of each MRD sense. An implementation of the method using definition sentences in the Longman Dictionary of Contemporary English is described. To this end, the topical word lists and topical cross-references in the Longman Lexicon of Contemporary English are used. We have conducted a series of experiments and evaluations to assess the performance of the proposed approach.
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Chen, J.N., Chang, J.S. (1999). Integrating Machine Readable Dictionary and Thesaurus for Conceptual Context Representation of Word Sense. In: Viegas, E. (eds) Breadth and Depth of Semantic Lexicons. Text, Speech and Language Technology, vol 10. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0952-1_10
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DOI: https://doi.org/10.1007/978-94-017-0952-1_10
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