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
N1 - Funding Information:
For performing classic retrieval, the system exploits Lucene which is a free information retrieval library, originally created in Java and is supported by Apache Software Foundation. Lucene scoring uses a combination of the Vector Space Model (VSM) and the Boolean model to determine how relevant a Document is to the User's query.
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
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 -