Word Sense Disambiguation: A comprehensive knowledge exploitation framework

29 Feb 2020  ·  Yinglin Wang, Ming Wang, Hamido Fujita ·

Word Sense Disambiguation (WSD) has been a basic and on-going issue since its introduction in natural language processing (NLP) community. Its application lies in many different areas including sentiment analysis, Information Retrieval (IR), machine translation and knowledge graph construction. Solutions to WSD are mostly categorized into supervised and knowledge-based approaches. In this paper, a knowledge-based method is proposed, modeling the problem with semantic space and semantic path hidden behind a given sentence. The approach relies on the well-known Knowledge Base (KB) named WordNet and models the semantic space and semantic path by Latent Semantic Analysis (LSA) and PageRank respectively. Experiments has proven the method’s effectiveness, achieving state-of-the-art performance in several WSD datasets.

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Word Sense Disambiguation Knowledge-based: KEF All 68.0 # 1
Senseval 2 69.6 # 1
Senseval 3 66.1 # 2
SemEval 2007 56.9 # 1
SemEval 2013 68.4 # 1
SemEval 2015 72.3 # 1


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