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 (US)
Title of host publicationProceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23
Pages289-294
Number of pages6
StatePublished - Oct 19 2010
Event23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 - Daytona Beach, FL, United States
Duration: May 19 2010May 21 2010

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
Period5/19/105/21/10

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering

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