本文在统计语言模型构造中,提出了将词间距离信息结合到N-gram统计语言模型中的思路,并称之为距离加权的关联词统计语言模型。该模型可以考虑一个句子中非相邻词之间的关系,基于“词距越近关系越密切”的原则,通过距离加权函数来引入距离信息,提高模型的预测能力。本文还将其应用到一个中文整句拼音输入法系统中。实验表明,该模型与传统的N-gram统计语言模型相比,汉字误识率有所降低,模型性能有了一定提高。
Abstract
Proposed in this paper is a novel language model based on the traditional N-gram model , where the inter-word distance information is integrated ,and therefore the model is referred to as the distance-weighted statistical language model. In this model , the relationship between disconnected words is taken into consideration. Based on the principle that closer words (in distance) have a closer relation. A distance-weighted function has been used to integrate the information so as to improve the model’s prediction ability. Compared with the original n-gram model ,the experiments results show that the proposed language model will reduce the Chinese whole sentence IME system’s word error rate.
关键词
N-gram /
关联词模型 /
距离加权 /
数据平滑
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Key words
N-gram /
word related /
language model /
distance-weighted /
model smooth
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参考文献
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脚注
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