Using rejuvenation to improve particle filtering for Bayesian word segmentation

Authors:
Benjamin Börschinger;Mark Johnson
Affiliations:
Macquarie University, Sydney, Australia and Heidelberg University, Heidelberg, Germany;Macquarie University, Sydney, Australia
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

Citing 5
Cited 0

An Efficient, Probabilistically Sound Algorithm for Segmentation andWord Discovery

Machine Learning - Special issue on natural language learning
Nonparametric bayesian models of lexical acquisition

Nonparametric bayesian models of lexical acquisition
Improving nonparameteric Bayesian inference: experiments on unsupervised word segmentation with adaptor grammars

NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Bayesian unsupervised word segmentation with nested Pitman-Yor language modeling

ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Online Learning Mechanisms for Bayesian Models of Word Segmentation

Research on Language and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel extension to a recently proposed incremental learning algorithm for the word segmentation problem originally introduced in Goldwater (2006). By adding rejuvenation to a particle filter, we are able to considerably improve its performance, both in terms of finding higher probability and higher accuracy solutions.