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.Electronic supplementary material The online version of this article

BackgroundKnowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NLP), they have the potential to contribute to retrospective studies by unlocking important phenotyping information contained in the narrative content of electronic health records (EHRs). This work aims to develop an extensive information representation scheme for clinical information contained in EHR narratives, and to support secondary use of EHR narrative data to answer clinical questions.MethodsWe review recent work that proposed information representation schemes and applied them to the analysis of clinical narratives. We then propose a unifying scheme that supports the extraction of information to address a large variety of clinical questions.ResultsWe devised a new information representation scheme for clinical narratives that comprises 13 entities, 11 attributes and 37 relations. The associated annotation guidelines can be used to consistently apply the scheme to clinical narratives and are https://cabernet.limsi.fr/annotation_guide_for_the_merlot_french_clinical_corpus-Sept2016.pdf.ConclusionThe information scheme includes many elements of the major schemes described in the clinical natural language processing literature, as well as a uniquely detailed set of relations.