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Spoken language understanding using finite state tagger and long-range dependency parsing

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2 Author(s)
Weidong Zhou ; Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China ; Baozong Yuan

Spoken language understanding is aimed at the interpretation of signs conveyed by a speech signal. While data-driven 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 expectation-maximization 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.

Published in:

Signal Processing (ICSP), 2010 IEEE 10th International Conference on

Date of Conference:

24-28 Oct. 2010