Enhancing text clustering by leveraging Wikipedia semantics
| Enhancing text clustering by leveraging Wikipedia semantics | |
| Author(s) | Jian Hu, Lujun Fang, Yang Cao, Hua-Jun Zeng, Hua Li, Qiang Yang, Zheng Chen |
| Published in | Unknown [+] |
| Date | 2008 |
| Volume | Unknown [+] |
| Issue | Unknown [+] |
| Page(s) | 179-186 |
| Keyword(s) | Unknown [+] |
| Peer-reviewed? | Unknown [+] |
| Language(s) | English |
| License(s) | Unknown [+] |
| Identifiers | |
| ISBN | Unknown [+] |
| DOI | 10.1145/1390334.1390367 |
| OCLC Number | Unknown [+] |
| CiteULike | 3112647 |
| arXiv | Unknown [+] |
| PubMed | Unknown [+] |
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| Dataset(s) | Unknown [+] |
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Enhancing text clustering by leveraging Wikipedia semantics is a 2008 publication written in English by Jian Hu, Lujun Fang, Yang Cao, Hua-Jun Zeng, Hua Li, Qiang Yang, Zheng Chen.
[edit] Abstract
Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved.
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