Preemptive information extraction using unrestricted relation discovery. Shinyama, Y. & Sekine, S. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pages 304-311, 2006. Association for Computational Linguistics.
Preemptive information extraction using unrestricted relation discovery [pdf]Paper  Preemptive information extraction using unrestricted relation discovery [link]Website  abstract   bibtex   
We are trying to extend the boundary of Information Extraction (IE) systems. Existing IE systems require a lot of time and human effort to tune for a new scenario. Preemptive Information Extraction is an attempt to automatically create all feasible IE systems in advance without human intervention. We propose a technique called Unrestricted Relation Discovery that discovers all possible relations from texts and presents them as tables. We present a preliminary system that obtains reasonably good results.
@inProceedings{
 title = {Preemptive information extraction using unrestricted relation discovery},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {304-311},
 issue = {June},
 websites = {http://portal.acm.org/citation.cfm?doid=1220835.1220874},
 publisher = {Association for Computational Linguistics},
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 created = {2010-11-06T02:48:29.000Z},
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 abstract = {We are trying to extend the boundary of Information Extraction (IE) systems. Existing IE systems require a lot of time and human effort to tune for a new scenario. Preemptive Information Extraction is an attempt to automatically create all feasible IE systems in advance without human intervention. We propose a technique called Unrestricted Relation Discovery that discovers all possible relations from texts and presents them as tables. We present a preliminary system that obtains reasonably good results.},
 bibtype = {inProceedings},
 author = {Shinyama, Yusuke and Sekine, Satoshi},
 booktitle = {Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics}
}
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