Domain-Specific Language Models and Lexicons for Tagging

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


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to .