Detecting Tables in HTML Documents
Table is a commonly used presentation scheme for describing relational information. Table understanding on the web has many potential applications including webmining, knowledge management, and webcon tent summarization and delivery to narrow-bandwidth devices. Although in HTML documents tables are generally marked as <table> elements, a <table> element does not necessarily indicate the presence of a genuine relational table. Thus the important first step in table understanding in the webdomain is the detection of the genuine tables. In our earlier work we designed a basic rule-based algorithm to detect genuine tables in major news and corporate home pages as part of a web content filtering system. In this paper we investigate a machine learning based approach that is trainable and thus can be automatically generalized to including any domain. Various features reflecting the layout as well as content characteristics of tables are explored. The system is tested on a large database which consists of 1, 393 HTML files collected from hundreds of different websites from various domains and contains over 10, 000 leaf <table> elements. Experiments were conducted using the cross validation method. The machine learning based approach outperformed the rule-based system and achieved an F-measure of 95.88%.
KeywordsContent Type Word Cluster Table Detection Major News Type Consistency
- 1.H.-H. Chen, S.-C. Tsai, and J.-H. Tsai: Mining Tables from Large Scale HTML Texts. In: The 18th Int. Conference on Computational Linguistics, Saarbrücken, Germany, July 2000.Google Scholar
- 2.G. Penn, J. Hu, H. Luo, and R. McDonald: Flexible Web Document Analysis for Delivery to Narrow-Bandwidth Devices. In: ICDAR2001, Seattle, WA, USA, September 2001.Google Scholar
- 3.M. Hurst: Layout and Language: Challenges for Table Understanding on the Web. In: First International Workshop on WebDocument Analysis, Seattle, WA, USA, September 2001, http://www.csc.liv.ac.uk/ wda2001.
- 4.M. Yoshida, K. Torisawa, and J. Tsujii: A Method to Integrate Tables of the World Wide Web. In: First International Workshop on Web Document Analysis, Seattle, WA, USA, September 2001, http://www.csc.liv.ac.uk/ wda2001/.
- 5.R. Haralick and L. Shapiro: Computer and Robot Vision. Addison Wesley, 1992.Google Scholar
- 6.T. Joachims: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In: The 14th International Conference on Machine Learning, Nashville, Tennessee, 1997.Google Scholar
- 7.Y. Yang and X. Liu: A Re-Examination of Text Categorization Methods, In: SIGIR’ 99, Berkeley, California, 1999.Google Scholar
- 8.D. Baker and A.K. McCallum: Distributional Clustering of Words for Text Classification, In: SIGIR’98, Melbourne, Australia, 1998.Google Scholar
- 9.M. F. Porter: An Algorithm for Suffix Stripping. In: Program, Vol. 14, no.3, 1980.Google Scholar
- 10.J. Hu, R. Kashi, D. Lopresti, G. Nagy, and G. Wilfong: Why Table Ground-Truthing is Hard. In: ICDAR2001, Seattle, WA, September 2001.Google Scholar
- 11.A. McCallum, K. Nigam, J. Rennie, and K. Seymore: Automating the Construction of Internet Portals with Machine Learning. In: Information Retrieval Journal, vol. 3, 2000.Google Scholar
- 12.D. Mladenic: Text-learning and related intelligent agents. In: IEEE Expert, July–August 1999.Google Scholar