Correlation Voting Fusion Strategy for Part of Speech Tagging

机译：语音标记的相关投票融合策略

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Having studied four corpus-based approaches to part of speech (POS) tagging, such as Transform-based Error Driven, the Decision Tree, Hidden Markov Model and Maximum Entropy, we present in this paper a novel data fusion strategy in POS tagging — Correlation Voting. Theoretical analysis and contrastive experiments with other fusion strategies show that linguistic knowledge for POS tagging can be more completely described by applying data fusion, and better tagging result can be achieved. The correlative voting is proved to be more outstanding than other fusion methods with a decrease of 27.85% in average tagging error rate.

机译：研究了四种基于语料库的语音部分（POS）标记方法，例如基于变换的错误驱动，决策树，隐马尔可夫模型和最大熵，本文提出了一种新颖的POS标记数据融合策略-相关性表决。理论分析和与其他融合策略的对比实验表明，通过应用数据融合可以更加完整地描述POS标记的语言知识，并且可以获得更好的标记结果。事实证明，相关投票比其他融合方法更为出色，平均标记错误率降低了27.85％。

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《International Conference on Signal Processing(ICSP'06); 20061116-20; Guilin(CN)》|2006年|P.1835-1838|共4页
会议地点 Guilin(CN)
作者
GUO Yonghui; Wu Baomin; Luo Changyuan; WANG Bingxi;
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Institute of Electronic Technology, Information Engineering University No. 12, Shangchengdong Road, Zhengzhou, Henan, China 450004;

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原文格式 PDF
正文语种 eng
中图分类通信理论;
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Correlation Voting Fusion Strategy for Part of Speech Tagging

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