Abstract

Many real-world texts contain tables. In order to process these texts correctly and extract the information contained within the tables, it is important to identify the presence and structure of tables. In this paper, we present a new approach that learns to recognize tables in free text, including the bound- ry, rows and columns of tables. When tested on Wall Street Journal news documents, our learning approach outperforms a deterministic table recognition algorithm that identifies tables based on a fixed set of conditions. Our learning approach is also more flexible and easily adaptable to texts in different do- mains with different table characteristics.

Citations

4072	JR: C4.5: Programs for Machine Learning - Quinlan
12	Layout and Language: lists and tables in technical documents - Douglas, Hurst - 1996
8	Layout & Language: Preliminary Experiments in Assigning Logical Structure to Table Cells - Hurst - 1997
3	Tutorial notes on building information extraction systems - Appelt, Israel - 1997
1	Using layout for the generation, understanding or retrieval of documents. Call for participation at the 1999 AAAI Fall Symposium Series - Power, Scott - 1999