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.
|Title of host publication||Bioinformatics|
|Subtitle of host publication||Concepts, Methodologies, Tools, and Applications|
|Number of pages||11|
|ISBN (Print)||1466636041, 9781466636040|
|Publication status||Published - 2013 Mar 31|
Bibliographical notePublisher Copyright:
© 2013, IGI Global.
All Science Journal Classification (ASJC) codes
- Computer Science(all)