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Return to Session VIa: Text Classification
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
@inproceedings{1009035, author = {Dou Shen and Zheng Chen and Qiang Yang and Hua-Jun Zeng and Benyu Zhang and Yuchang Lu and Wei-Ying Ma}, title = {Web-page classification through summarization}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {242--249}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009035}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
