@inproceedings{richard-etal-2024-fracas,
    title = "{FRACAS}: a {FR}ench Annotated Corpus of Attribution relations in new{S}",
    author = "Richard, Ange  and
      Alonzo Canul, Laura Cristina  and
      Portet, Fran{\c{c}}ois",
    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.654",
    pages = "7417--7428",
    abstract = "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 FRACAS, a manually annotated corpus of 1,676 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, the annotation process and give relevant statistics about our corpus. We give results for the inter-annotator agreement, which is substantially high for such a difficult linguistic phenomenon. We use this new resource to test the ability of a neural state-of-the-art relation extraction system to extract quotes and their source and we compare this model to the latest available system for quotation extraction for the French language, which is rule-based. Experiments using our dataset on the state-of-the-art system show very promising results considering the difficulty of the task at hand.",
}

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 FRACAS, a manually annotated corpus of 1,676 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, the annotation process and give relevant statistics about our corpus. We give results for the inter-annotator agreement, which is substantially high for such a difficult linguistic phenomenon. We use this new resource to test the ability of a neural state-of-the-art relation extraction system to extract quotes and their source and we compare this model to the latest available system for quotation extraction for the French language, which is rule-based. Experiments using our dataset on the state-of-the-art system show very promising results considering the difficulty of the task at hand.
Richard, Ange Alonzo Canul, Laura Cristina Portet, Fran{\c{c}}ois
FRACAS: a FRench Annotated Corpus of Attribution relations in newS