Information extraction from single and multiple sentences
Pages 875 - es
Abstract
Some Information Extraction (IE) systems are limited to extracting events expressed in a single sentence. It is not clear what effect this has on the difficulty of the extraction task. This paper addresses the problem by comparing a corpus which has been annotated using two separate schemes: one which lists all events described in the text and another listing only those expressed within a single sentence. It was found that only 40.6% of the events in the first annotation scheme were fully contained in the second.
References
[1]
H. Chieu and H. Ng. 2002. A Maximum Entroy Approach to Information Extraction from Semi-structured and Free Text. In Proceedings of the Eighteenth International Conference on Artificial Intelligence (AAAI-02), pages 768--791, Edmonton, Canada.
[2]
D. Fisher, S. Soderland, J. McCarthy, F. Feng, and W. Lehnert. 1995. Description of the UMass system as used for MUC-6. In Proceedings of the Sixth Message Understanding Conference (MUC-6), pages 221--236, San Francisco, CA.
[3]
R. Grishman and B. Sundheim. 1996. Message understanding conference - 6: A brief history. In Proceedings of the 16th International Conference on Computational Linguistics (COLING-96), pages 466--470, Copenhagen, Denmark.
[4]
S. Huttunen, R. Yangarber, and R. Grishman. 2002. Complexity of Event Structures in IE Scenarios. In Proceedings of the 19th International Conference on Computational Linguistics (COLING-2002), pages 376--382, Taipei, Taiwan.
[5]
MUC. 1995. Proceedings of the Sixth Message Understanding Conference (MUC-6), San Mateo, CA. Morgan Kaufmann.
[6]
S. Soderland. 1999. Learning Information Extraction Rules for Semi-structured and free text. Machine Learning, 31(1--3):233--272.
[7]
B. Sundheim. 1995. Overview of results of the MUC-6 evaluation. In Proceedings of the Sixth Message Understanding Conference (MUC-6), pages 13--31, Columbia, MA.
[8]
D. Zelenko, C. Aone, and A. Richardella. 2003. Kernel methods for relation extraction. Journal of Machine Learning Research, 3:1083--1106.
- Information extraction from single and multiple sentences
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Information
Published In
August 2004
1411 pages
Publisher
Association for Computational Linguistics
United States
Publication History
Published: 23 August 2004
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COLING '04 Paper Acceptance Rate 1,411 of 1,411 submissions, 100%;
Overall Acceptance Rate 1,537 of 1,537 submissions, 100%
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