@inproceedings{grouin-etal-2019-clinical,
title = "Clinical Case Reports for {NLP}",
author = "Grouin, Cyril and
Grabar, Natalia and
Claveau, Vincent and
Hamon, Thierry",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5029",
doi = "10.18653/v1/W19-5029",
pages = "273--282",
abstract = "Textual data are useful for accessing expert information. Yet, since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools. In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods. Our paper presents a corpus of clinical cases written in French, and their semantic annotations. Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations. The values of age, gender, and outcome are normalized. A subset with 70 files has been additionally manually annotated into 27 categories for a total number of 5,198 annotations.",
}
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<abstract>Textual data are useful for accessing expert information. Yet, since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools. In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods. Our paper presents a corpus of clinical cases written in French, and their semantic annotations. Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations. The values of age, gender, and outcome are normalized. A subset with 70 files has been additionally manually annotated into 27 categories for a total number of 5,198 annotations.</abstract>
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%0 Conference Proceedings
%T Clinical Case Reports for NLP
%A Grouin, Cyril
%A Grabar, Natalia
%A Claveau, Vincent
%A Hamon, Thierry
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F grouin-etal-2019-clinical
%X Textual data are useful for accessing expert information. Yet, since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools. In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods. Our paper presents a corpus of clinical cases written in French, and their semantic annotations. Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations. The values of age, gender, and outcome are normalized. A subset with 70 files has been additionally manually annotated into 27 categories for a total number of 5,198 annotations.
%R 10.18653/v1/W19-5029
%U https://aclanthology.org/W19-5029
%U https://doi.org/10.18653/v1/W19-5029
%P 273-282
Markdown (Informal)
[Clinical Case Reports for NLP](https://aclanthology.org/W19-5029) (Grouin et al., BioNLP 2019)
ACL
- Cyril Grouin, Natalia Grabar, Vincent Claveau, and Thierry Hamon. 2019. Clinical Case Reports for NLP. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 273–282, Florence, Italy. Association for Computational Linguistics.