Practical Use of Non-local Features for Statistical Spoken Language Understanding.
SCIE
SCOPUS
- Title
- Practical Use of Non-local Features for Statistical Spoken Language Understanding.
- Authors
- Minwoo Jeong; Lee, GG
- Date Issued
- 2008-04
- Publisher
- ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
- 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. (C) 2007 Elsevier Ltd. All rights reserved.
- Keywords
- Spoken language understanding; Non-local features; Long-distance dependency; Feature induction
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24998
- DOI
- 10.1016/j.csl.2007.07.001
- ISSN
- 0885-2308
- Article Type
- Article
- Citation
- COMPUTER SPEECH AND LANGUAGE, vol. 22, no. 2, page. 148 - 170, 2008-04
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