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Title: Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
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.
Authors:
; ; ;
Award ID(s):
1763618
Publication Date:
NSF-PAR ID:
10356091
Journal Name:
Proceedings of the 4th Workshop on Computational Models of Reference, Anaphora and Coreference (CRAC 2021)
Page Range or eLocation-ID:
100 to 110
Sponsoring Org:
National Science Foundation
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