TAILIEUCHUNG - Báo cáo khoa học: "Learning to Recognize Tables in Free Text"

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 boundary, 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. . | Learning to Recognize Tables in Free Text Hwee Tou Ng Chung Yong Lim Jessica Li Teng Koo DSO National Laboratories 20 Science Park Drive Singapore 118230 nhweetou Ichungyo kliteng @ 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 boundary 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 domains with different table characteristics. 1 Introduction Tables are present in many real-world texts. Some information such as statistical data is best presented in tabular form. A check on the more than 100 000 Wall Street Journal WSJ documents collected in the ACL DCI CD-ROM reveals that at least an estimated one in 30 documents contains tables. Tables present a unique challenge to information extraction systems. At the very least the presence of tables must be detected so that they can be skipped over. Otherwise processing the lines that constitute tables as if they are normal sentences is at best misleading and at worst may lead to erroneous analysis of the text. As tables contain important data and information it is critical for an information extraction system to be able to extract the information embodied in tables. This can be accomplished only if the structure of a table including its rows and columns are identified. That table recognition is an important step in information extraction has been recognized in Appelt and Israel 1997 . Recently there is also a greater realization within the computational linguistics community that the layout and types of .

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