Forbidden FRUIT is the Sweetest: An Annotated Tweets Corpus for French Unfrozen Idioms Identification

Multiword expressions (MWEs) are a key area of interest in NLP, studied across various languages and inspiring the creation of dedicated datasets and shared tasks such as PARSEME.Puns in multiword expressions (PMWEs) can be described as MWEs that have been "unfrozen" to acquire a new meaning or create wordplay.Unlike MWEs, they have received little attention in NLP, mainly due to the lack of resources available for their study.In this context, we introduce the French Unfrozen Idioms in Tweets (FRUIT) corpus, a dataset of tweets spanning three years and comprising 60,617 tweets containing both MWEs and PMWE candidates.We first describe the process of constructing this corpus, followed by an overview of the manual annotation task performed by three experts on 600 tweets, achieving an inter-annotator agreement score up to 0.83.Insights from this manual annotation process were then used to develop a Game With A Purpose (GWAP) to annotate more tweets from the FRUIT corpus.This GWAP aims to enhance players' understanding of MWEs and PMWEs.Currently, 13 players made 2,206 annotations on 931 tweets, reaching an score of 0.70.In total, 1,531 tweets from the FRUIT corpus have been annotated.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.law-1.6
Citations
1
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
2.48
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article

Forbidden FRUIT is the Sweetest: An Annotated Tweets Corpus for French Unfrozen Idioms Identification

Julien Bezançon, Gaël Lejeune, Marceau Hernandez, Antoine Gautier et al.
1 citations
Natural Language Processing Techniques
2.48
article

Forbidden FRUIT is the Sweetest: An Annotated Tweets Corpus for French Unfrozen Idioms Identification

Julien Bezançon, Gaël Lejeune, Marceau Hernandez, Antoine Gautier, Félix Alié
article en
1 citations

Abstract

Multiword expressions (MWEs) are a key area of interest in NLP, studied across various languages and inspiring the creation of dedicated datasets and shared tasks such as PARSEME.Puns in multiword expressions (PMWEs) can be described as MWEs that have been "unfrozen" to acquire a new meaning or create wordplay.Unlike MWEs, they have received little attention in NLP, mainly due to the lack of resources available for their study.In this context, we introduce the French Unfrozen Idioms in Tweets (FRUIT) corpus, a dataset of tweets spanning three years and comprising 60,617 tweets containing both MWEs and PMWE candidates.We first describe the process of constructing this corpus, followed by an overview of the manual annotation task performed by three experts on 600 tweets, achieving an inter-annotator agreement score up to 0.83.Insights from this manual annotation process were then used to develop a Game With A Purpose (GWAP) to annotate more tweets from the FRUIT corpus.This GWAP aims to enhance players' understanding of MWEs and PMWEs.Currently, 13 players made 2,206 annotations on 931 tweets, reaching an score of 0.70.In total, 1,531 tweets from the FRUIT corpus have been annotated.

Openalex Percentile: Top 9%
Natural Language Processing Techniques
2.48
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Forbidden FRUIT is the Sweetest: An Annotated Tweets Corpus for French Unfrozen Idioms Identification — Julien Bezançon, Gaël Lejeune, et al. · (2025) | TGRS Research Map | TGRS