Paper: Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task

ACL ID I05-1072
Title Principles of Non-stationary Hidden Markov Model and Its Applications to Sequence Labeling Task
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

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 th...