Infinite Hidden Markov Models and Applications in NLP

Add to your list(s) Download to your calendar using vCal

• Jurgen van Gael, Department of Engineering, University of Cambridge
• Friday 31 October 2008, 12:15-13:00
• SW01, Computer Laboratory.

If you have a question about this talk, please contact Johanna Geiss.

Since its invention 40 years ago, the Hidden Markov Model (HMM) has been successfully applied to domains such as vision, biology, natural language processing, etc. This success is arguably due to fast methods to do inference (forward-backward algorithm) and parameter learning (EM, Variational Bayes, etc.) In the standard supervised NLP application context, the number of hidden states (sometimes called the capacity of the HMM ) is chosen according to the (labelled) dataset used. Recent work (Goldwater & Griffiths 2007, Johnson 2007) has shown that unsupervised HMMs can be used efficiently to learn POS taggers from unlabelled data. However, the capacity used in that work is fixed in advance, which is not desirable when tackling new datasets/tasks and furthermore restricts the knowledge that can be learned from the data.

Recently, the machine learning community has turned its attention to nonparametric Bayesian methods. This framework allows us to treat the capacity of a model as a parameter which we want to learn. In this talk, I will introduce how nonparametric methods can be used to construct a nonparametric version of the HMM . I will compare the infinite HMM with other HMM models in the context of part-of-speech tagging.

This talk is part of the NLIP Seminar Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Department of Computer Science and Technology talks and seminars
• Graduate-Seminars
• Guy Emerson's list
• Interested Talks
• Language Sciences for Graduate Students
• NLIP Seminar Series
• PMRFPS's
• SW01, Computer Laboratory
• Simon Baker's List
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.