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Return to Categorization and supervised machine learning
This paper is concerned with automatic extraction of titles from the bodies of HTML documents. Titles of HTML documents should be correctly defined in the title fields; however, in reality HTML titles are often bogus. It is desirable to conduct automatic extraction of titles from the bodies of HTML documents. This is an issue which does not seem to have been investigated previously. In this paper, we take a supervised machine learning approach to address the problem. We propose a specification on HTML titles. We utilize format information such as font size, position, and font weight as features in title extraction. Our method significantly outperforms the baseline method of using the lines in largest font size as title (20.9%-32.6% improvement in F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (23.1% -29.0% improvements).
@inproceedings{1076079, author = {Yunhua Hu and Guomao Xin and Ruihua Song and Guoping Hu and Shuming Shi and Yunbo Cao and Hang Li}, title = {Title extraction from bodies of HTML documents and its application to web page retrieval}, booktitle = {SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval}, year = {2005}, isbn = {1-59593-034-5}, pages = {250--257}, location = {Salvador, Brazil}, doi = {http://doi.acm.org/10.1145/1076034.1076079}, publisher = {ACM Press}, address = {New York, NY, USA}, }  ©2006 Association for Computing Machinery |
