基于神经网络的汉语口语多义选择

汉语口语分析是交互式话语处理中的重要环节.在汉语中，有意义的最小单位是词，因此多义选择是口语分析系统必须首先解决的问题.该文提出了一种基于精简循环网络的汉语口语多义选择方法,并从词汇的语法、语义分类所固有的内在联系出发,给出了语法、语义的一致化处理策略.通过使用会面安排领域的口语语料进行实验,多义选择的开放测试的正确率为96.9%.

Word Sense Disambiguation of Spoken Chinese Using Neural Network

Spoken Chinese analysis lies in the center of interactive speech processing system.The smallest meaningful unit in Chinese language is the word,so word sense disambiguation is the basis of spoken Chinese analysis.In this paper, the authors propose a novel method for spoken Chinese word sense disambiguation based on a simple recurrent network. This method provides a consistent processing strategy for syntax and semantics according to the internal logic between the word syntactic classification and semantic classification. Applied in the corpus for meeting schedule, this method achieves an accuracy of 96.9% in an open testing of word sense disambiguation.