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 noteFunding Information:
Financial support from Academy of Finland is gratefully acknowledged (Grant Number 111692). The author would also like to thank Johnny Lindroos, Fredrick Sundell and Marketta Hiisa for their contribution to the project and their assistance in carrying out some of the experiments.
© 2013, IGI Global.
All Science Journal Classification (ASJC) codes
- Computer Science(all)