Open Access
Kumar, Saket
Graduate Program:
Computer Science
Master of Science
Document Type:
Master Thesis
Date of Defense:
March 24, 2015
Committee Members:
  • Omar A El Ariss, Thesis Advisor
  • Jeremy Joseph Blum, Thesis Advisor
  • Linda Marie Null, Thesis Advisor
  • Sukmoon Chang, Thesis Advisor
  • Thang Nguyen Bui, Thesis Advisor
  • Sense
  • Word
  • Disambiguation
  • Bee
  • Lesk
Humans can infer meaning through the use of not only the definition of a word, but also, where one word might have various conflicting definitions, based on their experiences and the text’s context and domain. Word Sense Disambiguation (WSD) is the problem of finding the most appropriate meaning of a word in a particular context. The functional importance of WSD lies in processing the sequence of words by pinpointing their meaning without the need for human intervention. It is crucial for many applications such as translation, summarization, information retrieval, and many other natural language applications. We introduce an unsupervised knowledge-source approach for word sense disambiguation using a bee colony optimization algorithm. We also present several variations to our bee colony approach that improve the overall performance of the algorithm. Our results are compared with recent unsupervised approaches such as ant colony optimization, genetic algorithms, most frequent sense, and simulated annealing.