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 - 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 |
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 |
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ASJC Scopus subject areas
- Software
Cite this
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
Y1 - 2012
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
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
ER -