@inproceedings{miculicich-henderson-2020-partially,
title = "Partially-supervised Mention Detection",
author = "Miculicich, Lesly and
Henderson, James",
editor = "Ogrodniczuk, Maciej and
Ng, Vincent and
Grishina, Yulia and
Pradhan, Sameer",
booktitle = "Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference",
month = dec,
year = "2020",
address = "Barcelona, Spain (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.crac-1.10",
pages = "91--98",
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.",
}
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%0 Conference Proceedings
%T Partially-supervised Mention Detection
%A Miculicich, Lesly
%A Henderson, James
%Y Ogrodniczuk, Maciej
%Y Ng, Vincent
%Y Grishina, Yulia
%Y Pradhan, Sameer
%S Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (online)
%F miculicich-henderson-2020-partially
%X 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.
%U https://aclanthology.org/2020.crac-1.10
%P 91-98
Markdown (Informal)
[Partially-supervised Mention Detection](https://aclanthology.org/2020.crac-1.10) (Miculicich & Henderson, CRAC 2020)
ACL
- Lesly Miculicich and James Henderson. 2020. Partially-supervised Mention Detection. In Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference, pages 91–98, Barcelona, Spain (online). Association for Computational Linguistics.