The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Comparative Study of an Unsupervised Word Sense Disambiguation Approach
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.
Related Content
|
Linkon Chowdhury, Md Sarwar Kamal, Shamim H. Ripon, Sazia Parvin, Omar Khadeer Hussain, Amira Ashour, Bristy Roy Chowdhury.
© 2024.
20 pages.
|
|
Mousomi Roy.
© 2024.
21 pages.
|
|
Nassima Dif, Zakaria Elberrichi.
© 2024.
20 pages.
|
|
Pyingkodi Maran, Shanthi S., Thenmozhi K., Hemalatha D., Nanthini K..
© 2024.
16 pages.
|
|
Mohamed Nadjib Boufenara, Mahmoud Boufaida, Mohamed Lamine Berkane.
© 2024.
16 pages.
|
|
Meroua Daoudi, Souham Meshoul, Samia Boucherkha.
© 2024.
25 pages.
|
|
Zhongyu Lu, Qiang Xu, Murad Al-Rajab, Lamogha Chiazor.
© 2024.
56 pages.
|
|
|