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- W2030854536 abstract "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." @default.
- W2030854536 created "2016-06-24" @default.
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- W2030854536 date "2010-10-01" @default.
- W2030854536 modified "2023-10-16" @default.
- W2030854536 title "Spoken language understanding using finite state tagger and long-range dependency parsing" @default.
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- W2030854536 doi "https://doi.org/10.1109/icosp.2010.5657129" @default.
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