A Joint Model for Entity Analysis: Coreference, Typing, and Linking

Greg Durrett, Dan Klein


Abstract
We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.
Anthology ID:
Q14-1037
Volume:
Transactions of the Association for Computational Linguistics, Volume 2
Month:
Year:
2014
Address:
Cambridge, MA
Editors:
Dekang Lin, Michael Collins, Lillian Lee
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
477–490
Language:
URL:
https://aclanthology.org/Q14-1037
DOI:
10.1162/tacl_a_00197
Bibkey:
Cite (ACL):
Greg Durrett and Dan Klein. 2014. A Joint Model for Entity Analysis: Coreference, Typing, and Linking. Transactions of the Association for Computational Linguistics, 2:477–490.
Cite (Informal):
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (Durrett & Klein, TACL 2014)
Copy Citation:
PDF:
https://aclanthology.org/Q14-1037.pdf
Data
OntoNotes 5.0