A Stochastic Technique to Obtain Training Data for Word Segmentation

Unlike western languages, there exists no word boundary in Japanese. This is why we face to hard problems to analyze documents in Japanese very often. More difficulty arises in expertised domains such as medical, mechanical, computer science documents. In this work, we discuss how to obtain pseudo t...

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Bibliographic Details
Published in:	Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Vol. 3; pp. 283 - 286
Main Authors:	Fukuda, Takuya, Miura, Takao
Format:	Conference Proceeding
Language:	English
Published:	Washington, DC, USA IEEE Computer Society 15.09.2009 IEEE
Series:	ACM Conferences
Subjects:	Computing methodologies > Artificial intelligence > Natural language processing Computing methodologies > Machine learning Computing methodologies > Machine learning > Learning paradigms Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies Markov Chain Monte Carlo (MCMC) method Mathematics of computing > Probability and statistics > Probabilistic algorithms Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Markov-chain Monte Carlo methods Mathematics of computing > Probability and statistics > Probabilistic reasoning algorithms > Sequential Monte Carlo methods Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks Mathematics of computing > Probability and statistics > Stochastic processes Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes Stochastic processes Stochastic Techniques Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes Training data Word Segmentation Word Segmentation Stochastic Techniques Markov Chain Monte Carlo (MCMC) method
ISBN:	0769538010, 9780769538013
Online Access:	Get full text
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