Monitoring free-text data using medical language processing
Computers and Biomedical Research
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Domain-specific language models and lexicons for tagging
Journal of Biomedical Informatics
The importance of the lexicon in tagging biological text
Natural Language Engineering
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
High-performance tagging on medical texts
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Creating a test corpus of clinical notes manually tagged for part-of-speech information
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Subdomain adaptation of a POS tagger with a small corpus
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
Adaptation of POS tagging for multiple BioMedical domains
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
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A difficulty with part-of-speech (POS) tagging of biomedical text is accessing and annotating appropriate training corpora. The latter may result in POS taggers trained on corpora that differ from the tagger's target biomedical text. In such cases where training and target corpora differ tagging accuracy decreases. We present a POS tagger that is more accurate than two frequently used biomedical POS taggers (Brill and TnT) when trained on a non-biomedical corpus and evaluated on the MedPost corpus (our tagger: 81.0%, Brill: 77.5%, TnT: 78.2%). Our tagger is also significantly faster than the next best tagger (TnT). It estimates a tag's likelihood for a token by combining prior probabilities (using existing methods) and token probabilities calculated in part using a Naive Bayes classifier. Our results suggest that 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.