FRACAS: A FRench Annotated Corpus of Attribution relations in newS
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Abstract (arXiv)
Quotation extraction is a widely useful task both from a sociological and from a Natural Language Processing perspective. However, very little data is available to study this task in languages other than English. In this paper, we present a manually annotated corpus of 1676 newswire texts in French for quotation extraction and source attribution. We first describe the composition of our corpus and the choices that were made in selecting the data. We then detail the annotation guidelines and annotation process, as well as a few statistics about the final corpus and the obtained balance between quote types (direct, indirect and mixed, which are particularly challenging). We end by detailing our inter-annotator agreement between the 8 annotators who worked on manual labelling, which is substantially high for such a difficult linguistic phenomenon.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 19 Sep 2023.
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13 of 52 references.
References · 13
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 719
- 2019Entity, Relation, and Event Extraction with Contextualized Span Representationscited by 261
- 2019DocRED: A Large-Scale Document-Level Relation Extraction Datasetcited by 239
- 2020Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoderscited by 87
- 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
- 2023DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extractioncited by 37
- 2022DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Populationcited by 17
- 2023REDrm FM: a Filtered and Multilingual Relation Extraction Datasetcited by 6
- 2022SumREN: Summarizing Reported Speech about Events in Newscited by 4
- 2020Exploration and Discovery of the COVID-19 Literature through Semantic Visualizationcited by 3
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.