Spoken Language Understanding Using Finite State Tagger and Long-range Dependency Parsing

摘要：

Spoken language understanding is aimed at the interpretation of signs conveyed by a speech signal. While datadriven methods reduce maintenance and portability costs compared with handcrafted parsers, the collection of word-level semantic annotations for training remains a time-consuming task. This paper has focused on building generative model “Finite State Tagger from unaligned data, using expectationmaximization techniques to align semantic concepts. Moreover, to model the hierarchical semantic relations in different slot entities, this paper proposed a pipeline architecture using finite state tagger and long-range dependency parsing.

关键词： semantic analysis spoken dialogue systems spoken language understanding

作者: Weidong Zhou Baozong, Yuan

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing, China, 100044 College of Inf Institute of Information Science, Beijing Jiaotong University, Beijing, China, 100044

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1305-1308

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）