@inproceedings{rigouts-terryn-etal-2020-termeval,
title = "{T}erm{E}val 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research ({ACTER}) Dataset",
author = "Rigouts Terryn, Ayla and
Hoste, Veronique and
Drouin, Patrick and
Lefever, Els",
editor = "Daille, B{\'e}atrice and
Kageura, Kyo and
Terryn, Ayla Rigouts",
booktitle = "Proceedings of the 6th International Workshop on Computational Terminology",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.computerm-1.12/",
pages = "85--94",
language = "eng",
ISBN = "979-10-95546-57-3",
abstract = "The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of \textit{heart failure} was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants."
}
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<abstract>The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.</abstract>
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%0 Conference Proceedings
%T TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset
%A Rigouts Terryn, Ayla
%A Hoste, Veronique
%A Drouin, Patrick
%A Lefever, Els
%Y Daille, Béatrice
%Y Kageura, Kyo
%Y Terryn, Ayla Rigouts
%S Proceedings of the 6th International Workshop on Computational Terminology
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-57-3
%G eng
%F rigouts-terryn-etal-2020-termeval
%X The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.
%U https://aclanthology.org/2020.computerm-1.12/
%P 85-94
Markdown (Informal)
[TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset](https://aclanthology.org/2020.computerm-1.12/) (Rigouts Terryn et al., CompuTerm 2020)
ACL