Building Domain-Specific Taggers without Annotated (Domain) Data

Part of speech tagging is a fundamental component in many NLP systems. When taggers developed in one domain are used in another domain, the performance can degrade considerably. We present a method for developing taggers for new domains without requiring POS annotated text in the new domain. Our method involves using raw domain text and identifying related words to form a domain specific lexicon. This lexicon provides the initial lexical probabilities for EM training of an HMM model. We evaluate the method by applying it in the Biology domain and show that we achieve results that are comparable with some taggers developed for this domain.