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A language model adaptation approach based on text classification

机译:基于文本分类的语言模型自适应方法

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摘要

In our paper, we divide the corpus into 8 domains through text classification using K-means algorithm, and calculate the trigram LMs for each one. But the experiment shows the performance in some ones becomes worse. In order to solve this problem, we try to do the LM adaptation based on the domain LMs. The adaptation is done by mixing the domain LMs with the background LM by a linear interpolation. Relative word error rate reductions of between 5 and 10
机译:在本文中,我们使用K-means算法通过文本分类将语料库划分为8个域,并为每个域计算三字组LM。但是实验表明,某些产品的性能会变差。为了解决这个问题,我们尝试基于域LM进行LM自适应。通过线性插值将域LM与背景LM混合来完成自适应。相对误码率降低5到10

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