Study of Topic Life Cycle Based on Hierarchical HMM

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This paper presents a topic ontology tree based hierarchical HMM (hHMM), and the generation of topic property based life cycle curve is studied. Compared with the traditional methods, this model can describe the hierarchy relations of topics.

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1324-1327

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November 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] L. E. Baum and T. Petrie: Statistical Inference for Probabilistic Functions of Finite State Markov Chains. The Annals of Mathematical Statistics, Vol. 37, no. 6(1966), pp.1554-1563.

DOI: 10.1214/aoms/1177699147

Google Scholar

[2] J. Baker: The DRAGON system-An overview. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 23, no. 1(1975), pp.24-29.

DOI: 10.1109/tassp.1975.1162650

Google Scholar

[3] X. Huang, M. Jack, and Y, Ariki: Hidden Markov Models for Speech Recognition. Edinburgh University Press(1990).

Google Scholar

[4] C. M. Bishop and E. Thompson, Maximum likelihood alignment of DNA sequences, Journal of Molecular Biology, Vol. 190, no. 2(1986), pp.159-165.

DOI: 10.1016/0022-2836(86)90289-5

Google Scholar

[5] Q. Mei and C. Zhai: Discovering evolutionary theme patterns from text-An Exploration of Temporal Text Mining, in Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD'05, ( 2005), pp.198-207.

DOI: 10.1145/1081870.1081895

Google Scholar