document: A machine learning based approach for table detection on the web Table is a commonly used presentation scheme , especially for describing relational information . However , table understanding remains an open problem . In this paper , we consider the problem of table detection in web documents . Its potential applications include web mining , knowledge management , and web content summarization and delivery to narrow-bandwidth devices . We describe a machine learning based approach to classify each given table entity as either genuine or non-genuine . Various features reflecting the layout as well as content characteristics of tables are studied . In order to facilitate the training and evaluation of our table classifier , we designed a novel web document table ground truthing protocol and used it to build a large table ground truth database . The database consists of 1,393 HTML files collected from hundreds of different web sites and contains 11,477 leaf TABLE elements , out of which 1,740 are genuine tables . Experiments were conducted using the cross validation method and an F-measure of 95.89 % was achieved .
doc_bio_tags: O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O