基于语义分类树的汉语口语理解方法

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。

面向物联网的语义空间资源索引构建及其查询优化算法

面对物联网中的海量空间资源,在空间资源索引的构建中引入语义分类的方法,使得传统的IR树具备语义特征,支持用户对资源的语义查询,并提出一种top-k语义查询优化算法,通过对实际数据的使用和实验分析,得出索引结构的有效性和查询结果的正确性,从索引构建时间、查询时间和查询准确率3个指标来评估算法的性能,实验结果表明,算法支持语义空间资源索引构建,并且查询性能好,准确率高。

Hierarchical Semantic-Category-Tree Model for Chinese-English Machine Translation

We introduce a novel Sermntic-Category- Tree （SCT） model to present the sen-antic structure of a sentence for Chinese-English Machine Translation （MT）. We use the SCT model to handle the reordering in a hierarchical structure in which one reordering is dependent on the others. Different from other reordering approaches, we handle the reordering at three levels： sentence level, chunk level, and word level. The chunk-level reordering is dependent on the sentence-level reordering, and the word-level reordering is dependent on the chunk-level reordering. In this paper, we formally describe the SCT model and discuss the translation strategy based on the SCT model. Further, we present an algorithm for analyzing the source language in SCT and transforming the source SCT into the target SCT. We apply the SCT model to a role-based patent text MT to evaluate the ability of the SCT model. The experimental results show that SCT is efficient in handling the hierarehical reordering operation in MT.