TY - GEN
T1 - Solving hard coreference problems
AU - Peng, Haoruo
AU - Khashabi, Daniel
AU - Roth, Dan
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics.
PY - 2015
Y1 - 2015
N2 - Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard,Winograd-style, pronoun resolution cases, while still performing at the stateof-the-art level on standard coreference resolution datasets.
AB - Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard,Winograd-style, pronoun resolution cases, while still performing at the stateof-the-art level on standard coreference resolution datasets.
UR - http://www.scopus.com/inward/record.url?scp=84960101902&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960101902&partnerID=8YFLogxK
U2 - 10.3115/v1/n15-1082
DO - 10.3115/v1/n15-1082
M3 - Conference contribution
AN - SCOPUS:84960101902
T3 - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 809
EP - 819
BT - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Y2 - 31 May 2015 through 5 June 2015
ER -