基于语义分类树的汉语口语理解方法

Approach to Spoken Chinese Understanding Based on Semantic Classification Trees

口语理解在口语自动翻译和人机对话系统中具有非常重要的作用。本文面向口语自动翻译提出了一种统计和规则相结合的汉语口语理解方法,该方法利用统计方法从训练语料中自动获取语义规则,生成语义分类树,然后利用语义分类树对待解析的汉语句子中与句子浅层语义密切相关的词语进行解析,最后再利用统计理解模型对各个词语的解析结果进行组合,从而获得整个句子的浅层语义领域行为。实验结果表明,该方法具有较高的准确率和鲁棒性,适合应用在限定领域的汉语口语浅层语义理解。

The spoken language understanding is a crucial part in spoken language translation systems and human-machine dialog systems. In this paper, we propose a new approach to spoken Chinese understanding which combines statistical and rule-based methods. In this approach, the semantic classification trees which are built by the semantic rules automatically learned from the training data are, used to disambiguate key words related to the sentences＇ shallow semantic meaning, and then, a statistical model is used to extract the whole sentence＇s domain action. The experimental results show that this approach has good performance and is feasible for the restricted domain oriented Chinese spoken language understanding in the shallow semantic level.