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- W2000793529 abstract "Spoken language understanding (SLU) addresses the problem of mapping natural language speech to frame structure encoding of its meaning. The statistical sequential labeling method has been successfully applied to SLU tasks; however, most sequential labeling approaches lack long-distance dependency information handling method. In this paper, we exploit non-local features as an estimate of long-distance dependencies to improve performance of the statistical SLU problem. A method we propose is to use trigger pairs automatically extracted by a feature induction algorithm. We describe a light practical version of the feature inducer for which a simple modification is efficient and successful. We evaluate our method on three SLU tasks and show an improvement of performance over the baseline local model." @default.
- W2000793529 created "2016-06-24" @default.
- W2000793529 creator A5043350987 @default.
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- W2000793529 date "2008-04-01" @default.
- W2000793529 modified "2023-09-26" @default.
- W2000793529 title "Practical use of non-local features for statistical spoken language understanding" @default.
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- W2000793529 doi "https://doi.org/10.1016/j.csl.2007.07.001" @default.
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