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 re ecting the layout as well as content characteristics of tables are studied. In order to facilitate the training and evaluation of our table classi er, 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 les collected from hundreds of di erent web sites and contains 11,477 leaf <TABLE> elements, out of which 1,740 are genuine tables. Experiments were conducted using the cross valida...

• Type: Conference
• Year: 2002
• Where: WWW
• Authors: Yalin Wang, Jianying Hu
• Keywords: Internet Technology, Keywords Table Detection, Table Ground Truth, Web Document Table, WWW 2002