@inproceedings{fang-etal-2021-chemu,
title = "{C}h{EMU}-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain",
author = "Fang, Biaoyan and
Druckenbrodt, Christian and
Akhondi, Saber A and
He, Jiayuan and
Baldwin, Timothy and
Verspoor, Karin",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.116",
doi = "10.18653/v1/2021.eacl-main.116",
pages = "1362--1375",
abstract = "Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.",
}
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<abstract>Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.</abstract>
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%0 Conference Proceedings
%T ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain
%A Fang, Biaoyan
%A Druckenbrodt, Christian
%A Akhondi, Saber A.
%A He, Jiayuan
%A Baldwin, Timothy
%A Verspoor, Karin
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F fang-etal-2021-chemu
%X Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.
%R 10.18653/v1/2021.eacl-main.116
%U https://aclanthology.org/2021.eacl-main.116
%U https://doi.org/10.18653/v1/2021.eacl-main.116
%P 1362-1375
Markdown (Informal)
[ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain](https://aclanthology.org/2021.eacl-main.116) (Fang et al., EACL 2021)
ACL
- Biaoyan Fang, Christian Druckenbrodt, Saber A Akhondi, Jiayuan He, Timothy Baldwin, and Karin Verspoor. 2021. ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1362–1375, Online. Association for Computational Linguistics.