title: Corpus-based identification and refinement of semantic classes.
abstract: Medical Language Processing (MLP), especially in specific domains, requires fine-grained semantic lexica. We examine whether robust natural language processing tools used on a representative corpus of a domain help in building and refining a semantic categorization. We test this hypothesis with ZELLIG, a corpus analysis tool. The first clusters we obtain are consistent with a model of the domain, as found in the SNOMED nomenclature. They correspond to coarse-grained semantic categories, but isolate as well lexical idiosyncrasies belonging to the clinical sub-language. Moreover, they help categorize additional words.
authors.affiliation: Laboratoire d'Informatique de Paris-Nord, Université Paris 13.
authors.forename: A
authors.initials: A
authors.lastname: Nazarenko
authors.forename: P
authors.initials: P
authors.lastname: Zweigenbaum
authors.forename: J
authors.initials: J
authors.lastname: Bouaud
authors.forename: B
authors.initials: B
authors.lastname: Habert
mesh_terms: D002965:Classification; D008037:Linguistics; D009323:Natural Language Processing; D012660:Semantics; D018875:Vocabulary, Controlled