Part-of-speech tagger based on maximum entropy model

Heyan Huang*, Xiaofei Zhang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

10 引用 (Scopus)

摘要

The maximum entropy (ME) conditional models don't force to adhere to the independence assumption such as in Hidden Markov generative models, and thus the ME -based Part-of-Speech (POS) tagger can depend on arbitrary, nonindependent features, which are benefit to the POS tagging, without accounting for the distribution of those dependencies. Since ME models are able to flexibly utilize a wide variety of features, the sparse problem of training data is efficiently solved. Experiments show that the POS tagging error rate is reduced by 54.25% in close test and 40.56% in open test over the Hidden-Markov-Model-based baseline, and synchronously an accuracy of 98.01% in close test and 95.56%in open test are obtained.

源语言英语
主期刊名Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
26-29
页数4
DOI
出版状态已出版 - 2009
活动2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009 - Beijing, 中国
期限: 8 8月 200911 8月 2009

出版系列

姓名Proceedings - 2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009

会议

会议2009 2nd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2009
国家/地区中国
Beijing
时期8/08/0911/08/09

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