A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT).
Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the cor...
| Publicado en: | Language Resources & Evaluation Vol. 52; no. 2; pp. 571 - 602 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Springer Nature
Jun2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129593476&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129593476 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2018 vid: 52 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 129593476 10.1007/s10579-017-9382-y ppf: 571 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.7MB tig: atl: A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT). aug: au: Campillos, Leonardo Deléger, Louise Grouin, Cyril Hamon, Thierry Ligozat, Anne-Laure Névéol, Aurélie affil: LIMSI-CNRS, Université Paris Saclay, 91403, Orsay, France su: Natural language processing French language Corpora Semantics Medical records sug: subj: Natural language processing French language Corpora Semantics Medical records keyword: Clinical narrative Inter-annotator agreement Personal health information Semantic annotations ab: Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2018. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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