Word sense disambiguation as a traveling salesman problem

  • Authors:

  • Kiem-Hieu Nguyen;Cheol-Young Ock

  • Affiliations:

  • School of Electrical Engineering, University of Ulsan, Ulsan, Korea 680---749;School of Electrical Engineering, University of Ulsan, Ulsan, Korea 680---749

  • Venue:
  • Artificial Intelligence Review
  • Year:
  • 2013

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Abstract

Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.