A Deductive Word Sense Disambiguation Approach Based on Data Mining and Knowledge Extraction in Expert Systems
Keywords:
Natural language processing, Expert system, Word sense disambiguation, Lexical ambiguity resolution, Forward chainingAbstract
Word Sense Disambiguation (WSD) involves assigning the appropriate sense to ambiguous words. WSD is one of the most challenging problems in several Natural Language Processing (NLP) tasks, such as machine translation. This paper proposes a novel approach consisting of four main components. In the first part, a mining process is used to construct a tree structure that represents helpful knowledge about the conceptual relationships between each ambiguous word and its relevant context. In the second part, a Knowledge Base (KB) is constructed based on the chains derived from the tree structure. The third part involves designing an expert system for lexical ambiguity resolution using the forward chaining strategy. In the final part, the KB is upgraded to improve its effectiveness in determining the correct senses of ambiguous words. The performance of the proposed approach is evaluated on the TWA corpus. The results demonstrate the effectiveness of the proposed expert system.
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Copyright (c) 2025 Zahra Pourbahman, Niloofar Rastin (Author); Mostafa Fakhrahmad
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