Learning to Extract Information From Text Based on User-Provided Examples.

Scott B. Huffman: Learning to Extract Information From Text Based on User-Provided Examples. CIKM 1996: 154-163

@inproceedings{DBLP:conf/cikm/Huffman96,
  author    = {Scott B. Huffman},
  title     = {Learning to Extract Information From Text Based on User-Provided
               Examples},
  booktitle = {CIKM '96, Proceedings of the Fifth International Conference on
               Information and Knowledge Management, November 12 - 16, 1996,
               Rockville, Maryland, USA},
  publisher = {ACM},
  year      = {1996},
  pages     = {154-163},
  ee        = {db/conf/cikm/Huffman96.html, http://doi.acm.org/10.1145/238355.238477},
  crossref  = {DBLP:conf/cikm/96},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

A growing population of users want to extract a growing variety of information from on-line texts. Unfortunately, current information extraction systems typically require experts to hand-build dictionaries of extraction patterns for each new type of information to be extracted. This paper presents a system that can learn dictionaries of extraction patterns directly from user-provided examples of texts and events to be extracted from them. The system, called LIEP, learns patterns that recognize relationships between key constituents based on local syntax. Patterns take the form of paths through a finite-state machine. Sets of patterns learned by LIEP for a sample extraction task perform nearly at the level of a hand-built dictionary of patterns.

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CIKM '96, Proceedings of the Fifth International Conference on Information and Knowledge Management, November 12 - 16, 1996, Rockville, Maryland, USA. ACM 1996
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