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SIGIR
2003
ACM

Table extraction using conditional random fields

9 years 7 months ago
Table extraction using conditional random fields
The ability to find tables and extract information from them is a necessary component of data mining, question answering, and other information retrieval tasks. Documents often contain tables in order to communicate densely packed, multi-dimensional information. Tables do this by employing layout patterns to efficiently indicate fields and records in two-dimensional form. Their rich combination of formatting and content present difficulties for traditional language modeling techniques, however. This paper presents the use of conditional random fields (CRFs) for table extraction, and compares them with hidden Markov models (HMMs). Unlike HMMs, CRFs support the use of many rich and overlapping layout and language features, and as a result, they perform significantly better. We show experimental results on plain-text government statistical reports in which tables are located with 92% F1, and their constituent lines are classified into 12 table-related categories with 94% accuracy. W...
David Pinto, Andrew McCallum, Xing Wei, W. Bruce C
Added 05 Jul 2010
Updated 05 Jul 2010
Type Conference
Year 2003
Where SIGIR
Authors David Pinto, Andrew McCallum, Xing Wei, W. Bruce Croft
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