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This paper reports on the 3rd CLEFeHealth evaluation lab, which continues our evaluation resource building activities for the medical domain. In this edition of the lab, we focus on easing patients and nurses in authoring, understanding, and accessing eHealth information. The 2015 CLEFeHealth evaluation lab was structured into two tasks, fo-cusing on(More)
In this paper, we present the methods we used to extract bacteria and biotopes names and then to identify the relation between those entities while participating to the BioNLP'13 Bacteria and Biotopes Shared Task. We used machine-learning based approaches for this task, namely a CRF to extract bacteria and biotopes names and a simple matching algorithm to(More)
OBJECTIVE While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as(More)
The evaluation of named entity recognition (NER) methods is an active field of research. This includes the recognition of named entities in speech transcripts. Evaluating NER systems on automatic speech recognition (ASR) output whereas human reference annotation was prepared on clean manual transcripts raises difficult alignment issues. These issues are(More)
This paper reports on Task 1b of the 2015 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs by considering ten types of entities including disorders, that were to be extracted from biomedical text in French. The task consisted of two phases: entity recognition (phase 1), in which(More)
This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with named entity recognition and normalization in French narratives, as offered in CLEF eHealth 2015. Named entity recognition involved ten types of entities including(More)
RÉSUMÉ Dans cet article, nous présentons la campagne 2012 du défi fouille de texte (DEFT). Cette édition traite de l'indexation automatique par des mots-clés d'articles scientifiques au travers de deux pistes. La première fournit aux participants la terminologie des mots-clés employés dans les documents à indexer tandis que la seconde ne fournit pas cette(More)
Very few datasets have been released for the evaluation of diagnosis coding with the International Classification of Diseases, and only one so far in a language other than English. This paper describes a large-scale dataset prepared from French death certificates, and the problems which needed to be solved to turn it into a dataset suitable for the(More)
Within the framework of the Quaero project, we proposed a new definition of named entities, based upon an extension of the coverage of named entities as well as the structure of those named entities. In this new definition, the extended named entities we proposed are both hierarchical and compositional. In this paper, we focused on the annotation of a(More)
We present in this article the methods we used for obtaining measures to ensure the quality and well-formedness of a text corpus. These measures allow us to determine the compatibility of a corpus with the treatments we want to apply on it. We called this method " certification of corpus ". These measures are based upon the characteristics required by the(More)