Erasmus MC at CLEF eHealth 2016: Concept Recognition and Coding in French Texts

 Improving the image quality by increasing the contrast in order to highlight of medical images features (shown by the arrows in Fig. 4a and Fig. Test images: a)

Extracting named entities using the algorithm proposed by the authors requires an initial search query which should contain an indicator of a particular named

and Zweigenbaum,P: CLEF eHealth 2018 Multilingual Information Extraction task Overview: ICD10 Coding of Death Certificates in French, Hungarian and Italian. and Bagheri, E.:

In this article, we focus on old French texts to evaluate the impact of manual and automatic normalisation before applying five geographical named entity recognition tools, as well

To address this need, we introduce a large high-quality corpus of clinical documents in French, annotated with a comprehensive scheme of entities, attributes and relations: the

The training data set corresponds to the QUAERO French Medical Corpus [11] previously used in CLEF 2015 eHealth task 1b [12] and made of two sub-corpora: EMEA: 6 drug inserts written

Finally, these sources provide comparable corpora, distinguished by their techni- cality, on different topics: medical topics in ency- clopedia, various drugs in drug leaflets,

The INRA systems (Bossy et al., 2009) use resources under the form of dictionaries of relevant names, such as gene and protein names for the medical domain, people,