Radiological Text Simplification Using a General Knowledge Base

Lionel Ramadier (1) , Mathieu Lafourcade (2)

1 CHRU Montpellier - Centre Hospitalier Régional Universitaire [Montpellier]
2 TEXTE - Exploration et exploitation de données textuelles

In the medical domain, text simplification is both a desirable and a challenging natural language processing task. Indeed, first, medical texts can be difficult to understand for patient, because of the presence of specialized medical terms. Replacing these difficult terms with easier words can lead to improve patient’s understanding. In this paper, we present a lexical network based method to simplify health information in French language. We deal with semantic difficulty by replacement difficult term with supposedly easier synonyms or by using semantically related term with the help of a French lexical semantic network. We extract semantic and lexical information present in the network. In this paper, we present such a method for text simplification along with its qualitative evaluation.

Mots clés: NLP; BioNLP; Text simplification

Domaines: Traitement du texte et du document

Lionel Ramadier, Mathieu Lafourcade. Radiological Text Simplification Using a General Knowledge Base. CICLing 2017 - 18th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2017, Budapest, Hungary. pp.617-627, ⟨10.1007/978-3-319-77116-8_46⟩. ⟨lirmm-03108571⟩