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Enhancing text clustering by leveraging wikipedia semantics (2008)
| Venue: | In SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval |
| Citations: | 40 - 1 self |
BibTeX
@INPROCEEDINGS{Hu08enhancingtext,
author = {Jian Hu and Lujun Fang and Yang Cao and Hua-jun Zeng and Hua Li and Qiang Yang and Zheng Chen},
title = {Enhancing text clustering by leveraging wikipedia semantics},
booktitle = {In SIGIR ’08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval},
year = {2008},
pages = {179--186},
publisher = {ACM}
}
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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.
Keyphrases
text clustering wikipedia semantics semantic relation traditional content similarity measure text representation optimized weight associative relation key term important information data user semantic relationship frequency statistic concept thesaurus wikipedia thesaurus traditional text clustering method external resource several method previous method ohsumed datasets associative concept document term text representation enrichment strategy unified framework experimental result effective word-sense disambiguation ability
