@inproceedings{le-cloirec-ait-yahya-etal-2024-frend,
title = "{FR}e{ND}: A {F}rench Resource of Negation Data",
author = "Le Cloirec - Ait Yahya, Hafida and
Seminck, Olga and
Amsili, Pascal",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.658/",
pages = "7461--7468",
abstract = "FReND is a freely available corpus of French language in which negations are hand-annotated. Negations are annotated by their cues and scopes. Comprising 590K tokens and over 8.9K negations, it is the largest dataset available for French. A variety of types of textual genres are covered: literature, blog posts, Wikipedia articles, political debates, clinical reports and newspaper articles. As the understanding of negation is not yet mastered by current state of the art AI-models, FReND is not only a valuable resource for linguistic research into negation, but also as training data for AI tasks such as negation detection."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="le-cloirec-ait-yahya-etal-2024-frend">
<titleInfo>
<title>FReND: A French Resource of Negation Data</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hafida</namePart>
<namePart type="family">Le Cloirec - Ait Yahya</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Olga</namePart>
<namePart type="family">Seminck</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pascal</namePart>
<namePart type="family">Amsili</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>FReND is a freely available corpus of French language in which negations are hand-annotated. Negations are annotated by their cues and scopes. Comprising 590K tokens and over 8.9K negations, it is the largest dataset available for French. A variety of types of textual genres are covered: literature, blog posts, Wikipedia articles, political debates, clinical reports and newspaper articles. As the understanding of negation is not yet mastered by current state of the art AI-models, FReND is not only a valuable resource for linguistic research into negation, but also as training data for AI tasks such as negation detection.</abstract>
<identifier type="citekey">le-cloirec-ait-yahya-etal-2024-frend</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.658/</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>7461</start>
<end>7468</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T FReND: A French Resource of Negation Data
%A Le Cloirec - Ait Yahya, Hafida
%A Seminck, Olga
%A Amsili, Pascal
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F le-cloirec-ait-yahya-etal-2024-frend
%X FReND is a freely available corpus of French language in which negations are hand-annotated. Negations are annotated by their cues and scopes. Comprising 590K tokens and over 8.9K negations, it is the largest dataset available for French. A variety of types of textual genres are covered: literature, blog posts, Wikipedia articles, political debates, clinical reports and newspaper articles. As the understanding of negation is not yet mastered by current state of the art AI-models, FReND is not only a valuable resource for linguistic research into negation, but also as training data for AI tasks such as negation detection.
%U https://aclanthology.org/2024.lrec-main.658/
%P 7461-7468
Markdown (Informal)
[FReND: A French Resource of Negation Data](https://aclanthology.org/2024.lrec-main.658/) (Le Cloirec - Ait Yahya et al., LREC-COLING 2024)
ACL
- Hafida Le Cloirec - Ait Yahya, Olga Seminck, and Pascal Amsili. 2024. FReND: A French Resource of Negation Data. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7461–7468, Torino, Italia. ELRA and ICCL.