Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text. Culotta, A., McCallum, A., & Betz, J. In Moore, R. C., Bilmes, J. A., Chu-Carroll, J., & Sanderson, M., editors, Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings (HLT/NAACL), June 4-9, 2006, New York, New York, USA, 2006. The Association for Computational Linguistics.
Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text [pdf]Paper  bibtex   
@inproceedings{DBLP:conf/naacl/CulottaMB06,
 author = {Aron Culotta and Andrew McCallum and Jonathan Betz},
 bibsource = {dblp computer science bibliography, http://dblp.org},
 biburl = {http://dblp.org/rec/bib/conf/naacl/CulottaMB06},
 booktitle = {Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings (HLT/NAACL), June 4-9, 2006, New York, New York, {USA}},
 editor = {Robert C. Moore and Jeff A. Bilmes and Jennifer Chu{-}Carroll and Mark Sanderson},
 url = {http://aclweb.org/anthology/N/N06/N06-1038.pdf},
 publisher = {The Association for Computational Linguistics},
 timestamp = {Mon, 19 Dec 2016 00:00:00 +0100},
 title = {Integrating Probabilistic Extraction Models and Data Mining to Discover Relations and Patterns in Text},
 year = {2006},
 sum = {Extract relations from Wikipedia articles. Run data mining on the relational graph to obtain patterns that are predictive of relations---such as "opponent of my opponent is my ally" and "a person is likely to have the same religion as their parents." Then use feaures derived from these patterns in a second run of extraction that improves accuracy.}
}
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