Joint entity and event coreference resolution across documents

Heeyoung Lee; Marta Recasens; Angel Chang; Mihai Surdeanu; Dan Jurafsky

Joint entity and event coreference resolution across documents

Heeyoung Lee, Marta Recasens, Angel Chang, Mihai Surdeanu, Dan Jurafsky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to model cluster merge operations. As clusters are built, information flows between entity and event clusters through features that model semantic role dependencies. Our system handles nominal and verbal events as well as entities, and our joint formulation allows information from event coreference to help entity coreference, and vice versa. In a cross-document domain with comparable documents, joint coreference resolution performs significantly better (over 3 CoNLL F1 points) than two strong baselines that resolve entities and events separately.

Original language	English (US)
Title of host publication	EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference
Pages	489-500
Number of pages	12
State	Published - Dec 1 2012
Externally published	Yes
Event	2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 - Jeju Island, Korea, Republic of Duration: Jul 12 2012 → Jul 14 2012

Publication series

Name	EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

Other

Other	2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012
Country	Korea, Republic of
City	Jeju Island
Period	7/12/12 → 7/14/12

ASJC Scopus subject areas

Software

Access to Document

Fingerprint Dive into the research topics of 'Joint entity and event coreference resolution across documents'. Together they form a unique fingerprint.

Cite this

Lee, H., Recasens, M., Chang, A., Surdeanu, M., & Jurafsky, D. (2012). Joint entity and event coreference resolution across documents. In EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference (pp. 489-500). (EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference).

Joint entity and event coreference resolution across documents. / Lee, Heeyoung; Recasens, Marta; Chang, Angel; Surdeanu, Mihai; Jurafsky, Dan.

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. 2012. p. 489-500 (EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, H, Recasens, M, Chang, A, Surdeanu, M & Jurafsky, D 2012, Joint entity and event coreference resolution across documents. in EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference, pp. 489-500, 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, Jeju Island, Korea, Republic of, 7/12/12.

Lee H, Recasens M, Chang A, Surdeanu M, Jurafsky D. Joint entity and event coreference resolution across documents. In EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. 2012. p. 489-500. (EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference).

Lee, Heeyoung ; Recasens, Marta ; Chang, Angel ; Surdeanu, Mihai ; Jurafsky, Dan. / Joint entity and event coreference resolution across documents. EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference. 2012. pp. 489-500 (EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference).

@inproceedings{80c3e113499f4e64b8df28a124793411,

title = "Joint entity and event coreference resolution across documents",

abstract = "We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to model cluster merge operations. As clusters are built, information flows between entity and event clusters through features that model semantic role dependencies. Our system handles nominal and verbal events as well as entities, and our joint formulation allows information from event coreference to help entity coreference, and vice versa. In a cross-document domain with comparable documents, joint coreference resolution performs significantly better (over 3 CoNLL F1 points) than two strong baselines that resolve entities and events separately.",

author = "Heeyoung Lee and Marta Recasens and Angel Chang and Mihai Surdeanu and Dan Jurafsky",

year = "2012",

month = dec,

day = "1",

language = "English (US)",

isbn = "9781937284435",

series = "EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference",

pages = "489--500",

booktitle = "EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference",

note = "2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012 ; Conference date: 12-07-2012 Through 14-07-2012",

}

TY - GEN

T1 - Joint entity and event coreference resolution across documents

AU - Lee, Heeyoung

AU - Recasens, Marta

AU - Chang, Angel

AU - Surdeanu, Mihai

AU - Jurafsky, Dan

PY - 2012/12/1

Y1 - 2012/12/1

N2 - We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to model cluster merge operations. As clusters are built, information flows between entity and event clusters through features that model semantic role dependencies. Our system handles nominal and verbal events as well as entities, and our joint formulation allows information from event coreference to help entity coreference, and vice versa. In a cross-document domain with comparable documents, joint coreference resolution performs significantly better (over 3 CoNLL F1 points) than two strong baselines that resolve entities and events separately.

AB - We introduce a novel coreference resolution system that models entities and events jointly. Our iterative method cautiously constructs clusters of entity and event mentions using linear regression to model cluster merge operations. As clusters are built, information flows between entity and event clusters through features that model semantic role dependencies. Our system handles nominal and verbal events as well as entities, and our joint formulation allows information from event coreference to help entity coreference, and vice versa. In a cross-document domain with comparable documents, joint coreference resolution performs significantly better (over 3 CoNLL F1 points) than two strong baselines that resolve entities and events separately.

UR - http://www.scopus.com/inward/record.url?scp=84881101926&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84881101926&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84881101926

SN - 9781937284435

T3 - EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

SP - 489

EP - 500

BT - EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

T2 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012

Y2 - 12 July 2012 through 14 July 2012

ER -