Diversified hidden Markov models for sequential labeling

Journal article


Qiao, Maoying, Bian, Wei, Da Xu, Richard Yi and Tao, Dacheng. (2015). Diversified hidden Markov models for sequential labeling. IEEE Transactions on Knowledge and Data Engineering. 27(11), pp. 2947-2960. https://doi.org/10.1109/TKDE.2015.2433262
AuthorsQiao, Maoying, Bian, Wei, Da Xu, Richard Yi and 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.

Year2015
JournalIEEE Transactions on Knowledge and Data Engineering
Journal citation27 (11), pp. 2947-2960
PublisherIEEE Computer Society
ISSN1041-4347
Digital Object Identifier (DOI)https://doi.org/10.1109/TKDE.2015.2433262
Scopus EID2-s2.0-84959567553
Research or scholarlyResearch
Page range2947-2960
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online14 May 2015
Publication process dates
Deposited30 Jul 2021
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