An Unsupervised Approach to Cluster Web Search Results based on Word Sense Communities
- Jiyang Chen
- Osmar R. Zaiane, University of Alberta (Database)
- Randy Goebel
Effectively organizing web search results into clusters is important to facilitate quick user navigation to relevant documents. Previous methods may rely on a training process and do not provide a measure for whether page clustering is actually required. In this paper, we reformalize the clustering problem as a word sense discovery problem. Given a query and a list of result pages, our unsupervised method detects word sense communities in the extracted keyword network. The documents are assigned to several refined word sense communities to form clusters. We use the modularity score of the discovered keyword community structure to measure page clustering necessity. Experimental results verify our method’s feasibility and effectiveness.
Citation
J. Chen, O. Zaiane, R. Goebel. "An Unsupervised Approach to Cluster Web Search Results based on Word Sense Communities". IEEE/WIC/ACM International Conference on Web Intelligence, Sydney, Australia, pp 725-729, December 2008.| Keywords: | |
| Category: | In Conference |
| Web Links: | IEEE |
BibTeX
@incollection{Chen+al:WI08,
author = {Jiyang Chen and Osmar R. Zaiane and Randy Goebel},
title = {An Unsupervised Approach to Cluster Web Search Results based on Word
Sense Communities},
Pages = {725-729},
booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence},
year = 2008,
}Last Updated: January 21, 2020Submitted by Sabina P
