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||Applied Natural Language Processing|
|Subtitle of host publication||Identification, Investigation and Resolution|
|Number of pages||11|
|Publication status||Published - 2011 Dec 1|
All Science Journal Classification (ASJC) codes
- Computer Science(all)