期刊
JOURNAL OF BIOMEDICAL INFORMATICS
卷 38, 期 6, 页码 422-430出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2005.02.009
关键词
clinical report analysis.; part-of-speech tagging accuracy; domain adaptation; clinical information systems; biomedical domain; corpus linguistics; statistical part-of-speech tagging; hidden Markov model
Accurate and reliable part-of-speech tagging is useful for many Natural Language Processing (NLP) tasks that form the foundation of NLP-based approaches to information retrieval and data mining. In general, large annotated corpora are necessary to achieve desired part-of-speech tagger accuracy. We show that a large annotated general-English corpus is not sufficient for building a part-of-speech tagger model adequate for tagging documents from the medical domain. However, adding a quite small domain-specific corpus to a large general-English one boosts performance to over 92% accuracy from 87% in our studies. We also suggest a number of characteristics to quantify the similarities between a training corpus and the test data. These results give guidance for creating an appropriate corpus for building a part-of-speech tagger model that gives satisfactory accuracy results on a new domain at a relatively small cost. (c) 2005 Elsevier Inc. All rights reserved.
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