Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

Nishant Yadav, Nicholas Monath, Rico Angell, Andrew McCallum


Abstract
Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models.
Anthology ID:
2021.crac-1.11
Volume:
Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
CRAC | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
100–110
Language:
URL:
https://aclanthology.org/2021.crac-1.11
DOI:
10.18653/v1/2021.crac-1.11
Bibkey:
Cite (ACL):
Nishant Yadav, Nicholas Monath, Rico Angell, and Andrew McCallum. 2021. Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions. In Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference, pages 100–110, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions (Yadav et al., CRAC 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.crac-1.11.pdf
Data
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