Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral French

Loïc Grobol


Abstract
We propose an end-to-end coreference resolution system obtained by adapting neural models that have recently improved the state-of-the-art on the OntoNotes benchmark to make them applicable to other paradigms for this task. We report the performances of our system on ANCOR, a corpus of transcribed oral French, for which it constitutes a new baseline with proper evaluation.
Anthology ID:
W19-2802
Volume:
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference
Month:
June
Year:
2019
Address:
Minneapolis, USA
Venue:
CRAC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8–14
Language:
URL:
https://aclanthology.org/W19-2802
DOI:
10.18653/v1/W19-2802
Bibkey:
Cite (ACL):
Loïc Grobol. 2019. Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral French. In Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference, pages 8–14, Minneapolis, USA. Association for Computational Linguistics.
Cite (Informal):
Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral French (Grobol, CRAC 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2802.pdf
Supplementary:
 W19-2802.Supplementary.tgz
Data
CoNLL-2012