A quantitative assessment of SENSATIONAL with an exploration of its applications

Wei Xiong, Min Song, Lori Watrous-deVersterre

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Word sense disambiguation is the problem of selecting a sense for a word from a set of predefined possibilities. This is a significant problem in the biomedical domain where a single word may be used to describe a gene, protein, or abbreviation. In this paper, we evaluate SENSATIONAL, a novel unsupervised WSD technique, in comparison with two popular learning algorithms, support vector machines (SVM) and K-means. Based on the accuracy measure, our results show that SENSATIONAL outperforms SVM and K-means by 2% and 17% respectively. In addition, we develop a polysemy-based search engine and an experimental visualization application that utilizes SENSATIONAL clustering technique.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Pages289-294
Number of pages6
Publication statusPublished - 2010 Oct 19
Event23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 - Daytona Beach, FL, United States
Duration: 2010 May 192010 May 21

Publication series

NameProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

Other

Other23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
CountryUnited States
CityDaytona Beach, FL
Period10/5/1910/5/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

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  • Cite this

    Xiong, W., Song, M., & Watrous-deVersterre, L. (2010). A quantitative assessment of SENSATIONAL with an exploration of its applications. In Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 (pp. 289-294). (Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23).