Abstract
Estimation of topology of probabilistic models provides us with an important technique for many statistical language processing tasks. In this investigation, we propose a new topology estimation method for Hierarchical Hidden Markov Model (HHMM) that generalizes Hidden Markov Model (HMM) in a hierarchical manner. HHMM is a stochastic model which has powerful description capability compared to HMM, but it is hard to estimate HHMM topology because we have to give an initial hierarchy structure in advance on which HHMM depends. In this paper we propose a recursive estimation method of HHMM submodels by using frequent similar subsequence sets. We show some experimental results to see the effectiveness of our method.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Akaike, H.: A new look at the statistical model identification. IEEE Trans. 19(6)
Brand, M., Oliver, N., Pentland, A.: Coupled hidden markov models for complex action recognition. In: CVPR 1997: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997), Washington, DC, USA, p. 994. IEEE Computer Society, Los Alamitos (1997)
Fine, S., Singer, Y.: The hierarchical hidden markov model: Analysis and applications. In: Machine Learning, pp. 41–62 (1998)
Katsuyuki, H.: On the problem in model selection of neural network regression in overrealizable scenario. Neural Computation 14(8), 1979–(2002)
Luhr, S., Bui, H.H., Venkatesh, S., West, G.A.: Recognition of human activity through hierarchical stochastic learning. In: IEEE International Conference on Pervasive Computing and Communications, vol. 0, p. 416 (2003)
Manning, C., Manning, C.D., Utze, H.S., The, M., Press, M., Lee, L.: Foundations of statistical natural language processing (1999)
Vogler, C., Metaxas, D.: Parallel hidden markov models for american sign language recognition. In: IEEE International Conference on Computer Vision, vol. 1, p. 116 (1999)
Wakabayashi, K., Miura, T.: Topics identification based on event sequence using co-occurrence words. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 219–225. Springer, Heidelberg (2008)
Wakabayashi, K., Miura, T.: Data stream prediction using incremental hidden markov models. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) Data Warehousing and Knowledge Discovery. LNCS, vol. 5691, pp. 63–74. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wakabayashi, K., Miura, T. (2010). Topology Estimation of Hierarchical Hidden Markov Models for Language Models. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds) Natural Language Processing and Information Systems. NLDB 2010. Lecture Notes in Computer Science, vol 6177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13881-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-13881-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13880-5
Online ISBN: 978-3-642-13881-2
eBook Packages: Computer ScienceComputer Science (R0)