@inproceedings{alkan-etal-2022-majority,
title = "A Majority Voting Strategy of a {S}ci{BERT}-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)",
author = "Alkan, Atilla Kaan and
Grouin, Cyril and
Schussler, Fabian and
Zweigenbaum, Pierre",
editor = "Ghosal, Tirthankar and
Blanco-Cuaresma, Sergi and
Accomazzi, Alberto and
Patton, Robert M. and
Grezes, Felix and
Allen, Thomas",
booktitle = "Proceedings of the first Workshop on Information Extraction from Scientific Publications",
month = nov,
year = "2022",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wiesp-1.17",
pages = "145--150",
abstract = "Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of multiple SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.",
}
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<abstract>Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of multiple SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.</abstract>
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%0 Conference Proceedings
%T A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)
%A Alkan, Atilla Kaan
%A Grouin, Cyril
%A Schussler, Fabian
%A Zweigenbaum, Pierre
%Y Ghosal, Tirthankar
%Y Blanco-Cuaresma, Sergi
%Y Accomazzi, Alberto
%Y Patton, Robert M.
%Y Grezes, Felix
%Y Allen, Thomas
%S Proceedings of the first Workshop on Information Extraction from Scientific Publications
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online
%F alkan-etal-2022-majority
%X Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of multiple SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.
%U https://aclanthology.org/2022.wiesp-1.17
%P 145-150
Markdown (Informal)
[A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)](https://aclanthology.org/2022.wiesp-1.17) (Alkan et al., WIESP 2022)
ACL