On Some Optimization Heuristics for Lesk-Like WSD Algorithms

  • Alexander Gelbukh
  • Grigori Sidorov
  • Sang-Yong Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3513)


For most English words, dictionaries give various senses: e.g., “bank”can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a given text has crucial importance in many applications of text processing, such as information retrieval or machine translation: e.g., “(my account in the) bank” is to be translated into Spanish as “(mi cuenta en el) banco” whereas “(on the) bank (of the lake)” as “(en la) orilla (del lago).” To choose the optimal combination of the intended senses of all words, Lesk suggested to consider the global coherence of the text, i.e., which we mean the average relatedness between the chosen senses for all words in the text. Due to high dimensionality of the search space, heuristics are to be used to find a near-optimal configuration. In this paper, we discuss several such heuristics that differ in terms of complexity and quality of the results. In particular, we introduce a dimensionality reduction algorithm that reduces the complexity of computationally expensive approaches such as genetic algorithms.


Travel Salesman Problem Machine Translation Word Sense Disambiguation Optimization Heuristic Sense Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alexander Gelbukh
    • 1
  • Grigori Sidorov
    • 1
  • Sang-Yong Han
    • 2
  1. 1.Natural Language and Text Processing LaboratoryCenter for Computing Research National Polytechnic InstituteMexico
  2. 2.Department of Computer Science and EngineeringChung-Ang UniversitySeoulKorea

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