Rapid Adaptation of POS Tagging for Domain Specific Uses
This addresses the need for efficient domain adaptation in NLP without manual annotation, though it is incremental as it builds on existing tagging techniques.
The paper tackles the problem of POS tagger performance degradation when applied to new domains by presenting an unsupervised adaptation methodology using suffix and orthographic information from raw text, achieving results comparable to domain-specific taggers in the Biological domain.
Part-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.