论文类型：期刊论文
发表刊物：CHINA COMMUNICATIONS
收录刊物：Scopus、SCIE
卷号：9
期号：3
页面范围：58-67
ISSN号：1673-5447
关键字：English PoS tagging; maximum entropy; rule-based approach; machine translation; NP chunking
摘要：A hybrid approach to English Part-of-Speech (PoS) tagging with its target application being English-Chinese machine translation in business domain is presented, demonstrating how a present tagger can be adapted to learn from a small amount of data and handle unknown words for the purpose of machine translation. A small size of 998 k English annotated corpus in business domain is built semiautomatically based on a new tagset; the maximum entropy model is adopted, and rule-based approach is used in post-processing. The tagger is further applied in Noun Phrase (NP) chunking. Experiments show that our tagger achieves an accuracy of 98.14%, which is a quite satisfactory result. In the application to NP chunking, the tagger gives rise to 2.21% increase in F-score, compared with the results using Stanford tagger.