title: Classification of health webpages as expert and non expert with a reduced set of cross-language features.
abstract: Making the distinction between expert and non expert health documents can help users to select the information which is more suitable for them, according to whether they are familiar or not with medical terminology. This issue is particularly important for the information retrieval area. In our work we address this purpose through stylistic corpus analysis and the application of machine learning algorithms. Our hypothesis is that this distinction can be performed on the basis of a small number of features and that such features can be language and domain independent. The used features were acquired in source corpus (Russian language, diabetes topic) and then tested on target (French language, pneumology topic) and source corpora. These cross-language features show 90% precision and 93% recall with non expert documents in source language; and 85% precision and 74% recall with expert documents in target language.
authors.affiliation: INSERM, UMR_S 872, Eq. 20, Paris, F-75006 France.
authors.forename: Natalia
authors.initials: N
authors.lastname: Grabar
authors.forename: Sonia
authors.initials: S
authors.lastname: Krivine
authors.forename: Marie-Christine
authors.initials: MC
authors.lastname: Jaulent
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006262:Health; D016247:Information Storage and Retrieval; D020407:Internet / Q000145:classification*; D019303:Multilingualism; D014175:Translating