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's clustering technique.
| Original language | English |
|---|---|
| Title of host publication | Applied Natural Language Processing |
| Subtitle of host publication | Identification, Investigation and Resolution |
| Publisher | IGI Global |
| Pages | 412-422 |
| Number of pages | 11 |
| ISBN (Print) | 9781609607418 |
| DOIs | |
| Publication status | Published - 2011 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
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A comparative study of an unsupervised word sense disambiguation approach. / Xiong, Wei; Song, Min; deDeversterre, Lori Watrous.
Applied Natural Language Processing: Identification, Investigation and Resolution. IGI Global, 2011. p. 412-422.Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - A comparative study of an unsupervised word sense disambiguation approach
AU - Xiong, Wei
AU - Song, Min
AU - deDeversterre, Lori Watrous
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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's clustering technique.
AB - 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's clustering technique.
UR - http://www.scopus.com/inward/record.url?scp=84899252465&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899252465&partnerID=8YFLogxK
U2 - 10.4018/978-1-60960-741-8.ch024
DO - 10.4018/978-1-60960-741-8.ch024
M3 - Chapter
AN - SCOPUS:84899252465
SN - 9781609607418
SP - 412
EP - 422
BT - Applied Natural Language Processing
PB - IGI Global
ER -