title: A token centric part-of-speech tagger for biomedical text.
abstract: OBJECTIVE
  Difficulties with part-of-speech (POS) tagging of biomedical text is accessing and annotating appropriate training corpora. These difficulties may result in POS taggers trained on corpora that differ from the tagger's target biomedical text (cross-domain tagging). In such cases where training and target corpora differ tagging accuracy decreases. This paper presents a POS tagger for cross-domain tagging called TcT.
  
  
  METHODS
  TcT estimates a tag's likelihood for a given token by combining token collocation probabilities and the token's tag probabilities calculated using a Naive Bayes classifier. We compared TcT to three POS taggers used in the biomedical domain (mxpost, Brill and TnT). We trained each tagger on a non-biomedical corpus and evaluated it on biomedical corpora.
  
  
  RESULTS
  TcT was more accurate in cross-domain tagging than mxpost, Brill and TnT (respective averages 83.9, 81.0, 79.5 and 78.8).
  
  
  CONCLUSIONS
  Our analysis of tagger performance suggests that lexical differences between corpora have more effect on tagging accuracy than originally considered by previous research work. Biomedical POS tagging algorithms may be modified to improve their cross-domain tagging accuracy without requiring extra training or large training data sets. Future work should reexamine POS tagging methods for biomedical text. This differs from the work to date that has focused on retraining existing POS taggers.
authors.affiliation: Department of Computer Science, University of Victoria, PO Box 3055, Victoria V8W 3P6, BC, Canada. Electronic address: nbarrett@uvic.ca.
authors.forename: Neil
authors.initials: N
authors.lastname: Barrett
authors.affiliation: Department of Computer Science, University of Victoria, PO Box 3055, Victoria V8W 3P6, BC, Canada. Electronic address: jens@uvic.ca.
authors.forename: Jens
authors.initials: J
authors.lastname: Weber-Jahnke
mesh_terms: D016013:Likelihood Functions; D008037:Linguistics; D008390:Markov Chains; D009323:Natural Language Processing; D009626:Terminology as Topic
keywords: Biomedical tagging evaluation; Cross-domain biomedical tagging; Part-of-speech tagging; Token-centric tagging