@inproceedings{alkan-etal-2022-tdac,
title = "{TDAC}, The First Corpus in Time-Domain Astrophysics: Analysis and First Experiments on Named Entity Recognition",
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-1.15",
pages = "131--139",
abstract = "The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation reports publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, the information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: the first Corpus in Time-Domain Astrophysics, based on observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP NLP Challenge.",
}
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<abstract>The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation reports publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, the information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: the first Corpus in Time-Domain Astrophysics, based on observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP NLP Challenge.</abstract>
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%0 Conference Proceedings
%T TDAC, The First Corpus in Time-Domain Astrophysics: Analysis and First Experiments on Named Entity Recognition
%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-tdac
%X The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation reports publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, the information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: the first Corpus in Time-Domain Astrophysics, based on observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP NLP Challenge.
%U https://aclanthology.org/2022.wiesp-1.15
%P 131-139
Markdown (Informal)
[TDAC, The First Corpus in Time-Domain Astrophysics: Analysis and First Experiments on Named Entity Recognition](https://aclanthology.org/2022.wiesp-1.15) (Alkan et al., WIESP 2022)
ACL