Tagging Medical Documents with High Accuracy

Abstract

AbstractWe ran both Brill’s rule-based tagger and TnT, a statistical tagger, with a default German newspaper-language model on a medical text corpus. Supplied with limited lexicon resources, TnT outperforms the Brill tagger with state-of-the-art performance figures (close to 97% accuracy). We then trained TnT on a large annotated medical text corpus, with a slightly extended tagset that captures certain medical language particularities, and achieved 98% tagging accuracy. Hence, statistical off-the-shelf POS taggers cannot only be immediately reused for medical NLP, but they also achieve – when trained on medical corpora – a higher performance level than for the newspaper genre.KeywordsTraining PointMedical CorpusMedical TextUnknown WordMedical DocumentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.