@inproceedings{sinha-etal-2023-shall,
title = "What shall we read : the article or the citations? - A case study on scientific language understanding",
author = "Sinha, Aman and
Bigeard, Sam and
Clausel, Marianne and
Constant, Mathieu",
editor = {Boudin, Florian and
Daille, B{\'e}atrice and
Dufour, Richard and
El, Oumaima and
Houbre, Ma{\"e}l and
Jourdan, L{\'e}ane and
Kooli, Nihel},
booktitle = "Actes de CORIA-TALN 2023. Actes de l'atelier ``Analyse et Recherche de Textes Scientifiques'' (ARTS)@TALN 2023",
month = "6",
year = "2023",
address = "Paris, France",
publisher = "ATALA",
url = "https://aclanthology.org/2023.jeptalnrecital-arts.14",
pages = "80--85",
abstract = "The number of scientific articles is increasing tremendously across all domains to such an extent that it has become hard for researchers to remain up-to-date. Evidently, scientific language understanding systems and Information Extraction (IE) systems, with the advancement of Natural Language Processing (NLP) techniques, are benefiting the needs of users. Although the majority of the practices for building such systems are data-driven, advocating the idea of {``}The more, the better{''}. In this work, we revisit the paradigm - questioning what type of data : text (title, abstract) or citations, can have more impact on the performance of scientific language understanding systems.",
}
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%0 Conference Proceedings
%T What shall we read : the article or the citations? - A case study on scientific language understanding
%A Sinha, Aman
%A Bigeard, Sam
%A Clausel, Marianne
%A Constant, Mathieu
%Y Boudin, Florian
%Y Daille, Béatrice
%Y Dufour, Richard
%Y El, Oumaima
%Y Houbre, Maël
%Y Jourdan, Léane
%Y Kooli, Nihel
%S Actes de CORIA-TALN 2023. Actes de l’atelier “Analyse et Recherche de Textes Scientifiques” (ARTS)@TALN 2023
%D 2023
%8 June
%I ATALA
%C Paris, France
%F sinha-etal-2023-shall
%X The number of scientific articles is increasing tremendously across all domains to such an extent that it has become hard for researchers to remain up-to-date. Evidently, scientific language understanding systems and Information Extraction (IE) systems, with the advancement of Natural Language Processing (NLP) techniques, are benefiting the needs of users. Although the majority of the practices for building such systems are data-driven, advocating the idea of “The more, the better”. In this work, we revisit the paradigm - questioning what type of data : text (title, abstract) or citations, can have more impact on the performance of scientific language understanding systems.
%U https://aclanthology.org/2023.jeptalnrecital-arts.14
%P 80-85
Markdown (Informal)
[What shall we read : the article or the citations? - A case study on scientific language understanding](https://aclanthology.org/2023.jeptalnrecital-arts.14) (Sinha et al., JEP/TALN/RECITAL 2023)
ACL