Domain Adaptation in Part-of-Speech Tagging

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Author(s): Miriam Lúcia Domingues (Federal University of Pará, Brazil)and Eloi Luiz Favero (Federal University of Pará, Brazil)
Copyright: 2013
Pages: 21
Source title: Emerging Applications of Natural Language Processing: Concepts and New Research
Source Author(s)/Editor(s): Sivaji Bandyopadhyay (Jadavpur University, India), Sudip Kumar Naskar (Dublin City University, Ireland)and Asif Ekbal (Indian Institute of Technology Patna, India)
DOI: 10.4018/978-1-4666-2169-5.ch003

Keywords: Artificial Intelligence / Computational Linguistics / Computer Science & IT / Information Science Reference

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Abstract

Many Natural Language Processing (NLP) applications rely on accuracy of the part-of-speech taggers. Although many taggers have good accuracy for the domain in which they were trained, their accuracy typically is not portable to new domains due to problems, such as different linguistic structures or presence of new words. The need for domain adaptation has emerged as a new challenge for part-of-speech tagging and in most NLP tasks. The goal of this chapter is to highlight solutions that handle labeled and unlabeled data, methods that deal with such data to solve the domain adaptation problem, and to present a case study that has achieved significant accuracy rates on tagging journalistic and scientific texts.

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Domain Adaptation in Part-of-Speech Tagging

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