Partially-supervised Mention Detection

Lesly Miculicich, James Henderson


Abstract
Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference resolution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.
Anthology ID:
2020.crac-1.10
Volume:
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference
Month:
December
Year:
2020
Address:
Barcelona, Spain (online)
Venues:
COLING | CRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
91–98
Language:
URL:
https://www.aclweb.org/anthology/2020.crac-1.10
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.crac-1.10.pdf