Spoken language understanding using the Hidden Vector State Model

By Yulan He, Susannah Young. 2006

108 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

A Joint Model of Language and Perception for Grounded Attribute Learning (2012, background): “There has been signiﬁcant work on supervised learning for inducing semantic parsers (Zelle & Mooney, 1996; He & Young, 2006; Wong & Mooney, 2007).”
An empirical investigation of sparse log-linear models for improved dialogue act classification (2013, methodology): “Early approaches start with using the language models [15, 16], and also include the use of generative models such as the source-channel model [17], hidden Markov models (HMM) [18, 19, 20], and the hidden vector state model [21].”
Lexical Generalization in CCG Grammar Induction for Semantic Parsing (2011, methodology): “For Atis, we report results from HY06 (He and Young, 2006), ZC07, and UBL.”
Spoken language understanding from unaligned data using discriminative classification models (2009, methodology): “In order to compare our results with previous work [4, 10, 12], we apply our method to the ATIS dataset [11].”
Online Learning of Relaxed CCG Grammars for Parsing to Logical Form (2007, methodology): “Recent work (Mooney, 2007; He and Young, 2006; Zettlemoyer and Collins, 2005) has developed learning algorithms for the problem of mapping sentences to underlying semantic representations.”
Bootstrapping Semantic Parsers from Conversations (2011, background): “…Mooney, 2006; Lu et al., 2008), inductive logic programming (Zelle and Mooney, 1996; Thompson and Mooney, 2003; Tang and Mooney, 2000), probabilistic push-down automata (He and Young, 2005; 2006) and ideas from support vector machines and string kernels (Kate and Mooney, 2006; Nguyen et al., 2006).”