@inproceedings{DBLP:conf/naacl/CulottaWM07,
 author = {Aron Culotta and Michael L. Wick and Andrew McCallum},
 bibsource = {dblp computer science bibliography, http://dblp.org},
 biburl = {http://dblp.org/rec/bib/conf/naacl/CulottaWM07},
 booktitle = {Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), Proceedings, April 22-27, 2007, Rochester, New York, {USA}},
 editor = {Candace L. Sidner and Tanja Schultz and Matthew Stone and ChengXiang Zhai},
 url = {http://www.aclweb.org/anthology/N07-1011},
 pages = {81--88},
 publisher = {The Association for Computational Linguistics},
 timestamp = {Thu, 15 Dec 2016 16:11:53 +0100},
 title = {First-Order Probabilistic Models for Coreference Resolution},
 year = {2007},
 sum  = {Traditional coreference uses features only over pairs of mentions. Here we present a conditional random field with first-order logic for expressing features, enabling features over sets of mentions. The result is a new state-of-the-art results on ACE 2004 coref, jumping from 69 to 79---a 45% reduction in error. The advance depends crucially on a new method of parameter estimation for such "weighted logic" models based on learning rankings and error-driven training.}
}

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