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Your search keyword '"Xiao, Jinghui"' showing total 2 results
Start Over Author "Xiao, Jinghui" Topic language model
2 results on '"Xiao, Jinghui"'

1. A Similarity-Based Approach to Data Sparseness Problem of Chinese Language Modeling

Author

Xiao, Jinghui, Liu, Bingquan, Wang, Xiaolong, Li, Bing, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gelbukh, Alexander, editor, de Albornoz, Álvaro, editor, and Terashima-Marín, Hugo, editor

Published
2005
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2. Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task

Author

Xiao Jinghui, Wang Xiaolong, and Liu Bingquan

Subjects
Computer science, Word error rate, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), Point (geometry), Language model, Parameter space, Hidden Markov model, Representation (mathematics), Algorithm, Sequence labeling
Abstract

Hidden Markov Model (Hmm) is one of the most popular language models. To improve its predictive power, one of Hmm hypotheses, named limited history hypothesis, is usually relaxed. Then Higher-order Hmm is built up. But there are several severe problems hampering the applications of high-order Hmm, such as the problem of parameter space explosion, data sparseness problem and system resource exhaustion problem. From another point of view, this paper relaxes the other Hmm hypothesis, named stationary (time invariant) hypothesis, makes use of time information and proposes a non-stationary Hmm (NSHmm). This paper describes NSHmm in detail, including its definition, the representation of time information, the algorithms and the parameter space and so on. Moreover, to further reduce the parameter space for mobile applications, this paper proposes a variant form of NSHmm (VNSHmm). Then NSHmm and VNSHmm are applied to two sequence labeling tasks: pos tagging and pinyin-to-character conversion. Experiment results show that compared with Hmm, NSHmm and VNSHmm can greatly reduce the error rate in both of the two tasks, which proves that they have much more predictive power than Hmm does.

Published
2005

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