摘要
提出了一个语义分析集成系统,并在此基础上构建了结构化的语言模型.该语义分析集成系统能够自动分析句子中各个词的词义以及词之间的语义依存关系,达到90.85%的词义标注正确率和75.84%的语义依存结构标注正确率.为了描述语言的结构信息和长距离依存关系,研究并分析了两种基于语义结构的语言模型.最后,在中文语音识别任务上测试两类语言模型的性能.与三元语言模型相比,性能最好的语义结构语言模型——中心词三元模型,使绝对字错误率下降0.8%,相对错误率下降8%.
An integrated semantic analysis system is presented, and the structured language models are proposed based on it. The semantic analysis system can automatically tag semantic class for each word and analyze the semantic dependency structure between words with the precision of 90.85% and 75.84% respectively. In order to describe sentence structure and long-distance dependency, two kinds of structured language models are examined and analyzed. Finally, these two language models are evaluated on the task of Chinese speech recognition. Experiments show that the best semantic structured language model-headword trigram model-achieves 0.8% absolute error reduction and 8% relative error reduction over the trigram model.
出处
《软件学报》
EI
CSCD
北大核心
2005年第9期1523-1533,共11页
Journal of Software
基金
国家高技术研究发展计划(863)~~
关键词
语义分析
依存分析
语言模型
语音识别
semantic analysis
dependency analysis
language model
speech recognition
