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Two-phase reanalysis model for understanding user intention
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AI
DOI：10.1016/j.patrec.2013.12.015.png)
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摘要
En 中文
This paper proposes a two-phase reanalysis model for understanding user intention in utterances, by considering the correlative characteristics between the three attributes relating to user intention. The proposed model comprises two phases. In the first phase, each attribute is analyzed in the optimized sequence. The results of the analysis are then used as features that undergo reanalysis in the second phase, with the assumption that the relationship between the attributes is correlative. The experiments conducted showed that the proposed model improves user intention analysis over the baseline model, with an error reduction rate in Speech Act, Concept Sequence, and Arguments of 0.64%, 14.78%, and 5.84%, respectively. (C) 2014 Published by Elsevier B.V.
Keyword:
Natural language processing
Dialogue system
Machine learning
User intention analysis
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
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