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Diversified hidden Markov models for sequential labeling
Qiao, Maoying Bian, Wei Da Xu, Richard Yi Tao, Dacheng
Qiao, Maoying
Bian, Wei
Da Xu, Richard Yi
Tao, Dacheng
Abstract
Labeling of sequential data is a prevalent meta-problem for a wide range of real world applications. While the first-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling, the basic model does not show satisfying performance when it is directly applied to real world problems, such as part-of-speech tagging (PoS tagging) and optical character recognition (OCR). Aiming at improving performance, important extensions of HMM have been proposed in the literatures. One of the common key features in these extensions is the incorporation of proper prior information. In this paper, we propose a new extension of HMM, termed diversified Hidden Markov Models (dHMM), which utilizes a diversity-encouraging prior over the statetransition probabilities and thus facilitates more dynamic sequential labellings. Specifically, the diversity is modeled by a continuous determinantal point process prior, which we apply to both unsupervised and supervised scenarios. Learning and inference algorithms for dHMM are derived. Empirical evaluations on benchmark datasets for unsupervised PoS tagging and supervised OCR confirmed the effectiveness of dHMM, with competitive performance to the state-of-the-art.
Keywords
Date
2015
Type
Journal article
Journal
IEEE Transactions on Knowledge and Data Engineering
Book
Volume
27
Issue
11
Page Range
2947-2960
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Open Access Status
License
All rights reserved
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Controlled