Miller, JohnBloodgood, MichaelTorii, ManabuVijay-Shanker, KPart-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are trained in one domain and applied in significantly different domains, their performance can degrade dramatically. We present a methodology for rapid adaptation of POS taggers to new domains. Our technique is unsupervised in that a manually annotated corpus for the new domain is not necessary. We use suffix information gathered from large amounts of raw text as well as orthographic information to increase the lexical coverage. We present an experiment in the Biological domain where our POS tagger achieves results comparable to POS taggers specifically trained to this domain.en-UScomputer sciencestatistical methodsartificial intelligencemachine learningcomputational linguisticsnatural language processinghuman language technologytext processingTransformation Based Learningpart-of-speech taggingPOS taggingdomain-specific POS taggingdomain-specific part-of-speech taggingdomain adaptationrapid adaptationrapid domain adaptationunsupervised domain adaptationBioNLPbiomedical natural language processingbiomedical text processingbiomedical POS taggingbiomedical part-of-speech taggingsuffix-based part-of-speech taggingsuffix-based POS taggingArticle