title: Sharing models and tools for processing German clinical texts.
abstract: The automatic processing of non-English clinical documents is massively hampered by the lack of publicly available medical language resources for training, testing and evaluating NLP components. We suggest sharing statistical models derived from access-protected clinical documents as a reasonable substitute and provide solutions for sentence splitting, tokenization and POS tagging of German clinical texts. These three components were trained on the confidential FRAMED corpus, a non-sharable collection of various German-language clinical document types. The models derived therefrom outperform alternative components from OPENNLP and the Stanford POS tagger, also trained on FRAMED.
authors.affiliation: Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Jena, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Hellrich
authors.affiliation: Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Jena, Germany.
authors.forename: Franz
authors.initials: F
authors.lastname: Matthies
authors.affiliation: Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Jena, Germany.
authors.forename: Erik
authors.initials: E
authors.lastname: Faessler
authors.affiliation: Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Jena, Germany.
authors.forename: Udo
authors.initials: U
authors.lastname: Hahn
mesh_terms: D005858:Germany; D000069550:Machine Learning; D008962:Models, Theoretical; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D012984:Software; D009626:Terminology as Topic; D014175:Translating; D018875:Vocabulary, Controlled