Contextual dependencies in unsupervised word segmentation

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

• Keith Vertanen (University of Cambridge)
• Monday 20 August 2007, 11:00-12:00
• TCM Seminar Room, Cavendish Laboratory, Department of Physics.

If you have a question about this talk, please contact Philip Sterne.

We will be discussing the paper “Contextual dependencies in unsupervised word segmentation” by Sharon Goldwater, Thomas L. Griffiths and Mark Johnson.

Available from: http://cocosci.berkeley.edu/tom/papers/wordseg1.pdf

Abstract: Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on sub-optimal search procedures.

This talk is part of the Machine Learning Journal Club series.

This talk is included in these lists:

• Cambridge talks
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group Journal Clubs
• Inference Group Summary
• Interested Talks
• ML
• Machine Learning Journal Club
• Machine Learning Summary
• Quantum Matter Journal Club
• TCM Seminar Room, Cavendish Laboratory, Department of Physics
• TQS Journal Clubs
• rp587
• yk373's list

Note that ex-directory lists are not shown.