Accurate and reliable part-of-speech tagging is a pre-requisite 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 tagger accuracy. We show that a large annotated general-English corpus is not sufficient for building a 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 tagger model that gives satisfactory accuracy results on a new domain at a relatively small cost.
By: Anni R. Coden, Serguei V. Pakhomov, Rie K. Ando, Patrick H. Duffy, Christopher G. Chute
Published in: RC23195 in 2004
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