Improved Word Confidence Estimation using Long Range Features

机译：使用远距离功能改进单词置信度估计

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摘要

This paper describes experiments in improving word confidence estimation using document- and task-level features of the hypothesized word sequence from a recognizer. The improved confidence estimates are shown to improve information extraction performance, specifically named entity (NE) recognition. The detected names can then be used to further improve confidence estimation in a multi-pass NE recognition framework.

机译：本文介绍了使用来自识别器的假设单词序列的文档级和任务级功能来提高单词置信度估计的实验。改进的置信度估计值可以改善信息提取性能，特别是命名实体（NE）识别。然后，检测到的名称可以用于在多遍NE识别框架中进一步改善置信度估计。

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来源
《European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK》|2001年|P.2117-2120|共4页
会议地点 Aalborg(DK);Aalborg(DK)
作者
David D. Palmer; Mari Ostendorf;
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作者单位

Electrical Engineering Dept., University of Washington, Seattle, WA 98195;

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原文格式 PDF
正文语种 eng
中图分类传播理论;
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3. A Word-Based Naïve Bayes Classifier for Confidence Estimation in Speech Recognition [J] . Sanchis A., Juan A., Vidal E. Audio, Speech, and Language Processing, IEEE Transactions on . 2012,第2期

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4. Improved Word Confidence Estimation using Long Range Features [C] . David D. Palmer, Mari Ostendorf European conference on speech communication and technology . 2001

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Improved Word Confidence Estimation using Long Range Features

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