图 书 馆
含噪声信道模型的无监督估计Unsupervised Estimation for Noisy-Channel Models |
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| 课程网址: | http://videolectures.net/icml07_mylonakis_uefn/ |
| 主讲教师: | Markos Mylonakis |
| 开课单位: | 阿姆斯特丹大学 |
| 开课时间: | 2007-07-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Shannon’s Noisy-Channel model, which describes how a corrupted message might be reconstructed, has been the corner stone for much work in statistical language and speech processing. The model factors into two components: a language model to characterize the original message and a channel model to describe the channel’s corruptive process. The standard approach for estimating the parameters of the channel model is unsupervised Maximum-Likelihood of the observation data, usually approximated using the Expectation-Maximization (EM) algorithm. In this paper we show that it is better to maximize the joint likelihood of the data at both ends of the noisy-channel. We derive a corresponding bi-directional EM algorithm and show that it gives better performance than standard EM on two tasks: (1) translation using a probabilistic lexicon and (2) adaptation of a part-of-speech tagger between related languages. |
| 关 键 词: | 信道模型; 工作统计语言; 语音处理; 参数估计 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-06：lxf |
| 阅读次数: | 32 |