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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| Veröffentlicht in: | Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Jg. 3; S. 283 - 286 |
|---|---|
| Hauptverfasser: | , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
Washington, DC, USA
IEEE Computer Society
15.09.2009
IEEE |
| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Computing methodologies
> Modeling and simulation
> Model development and analysis
> Modeling methodologies
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
|
| ISBN: | 0769538010, 9780769538013 |
| Online-Zugang: | Volltext |
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| Abstract | 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 test corpus based on Markov process Monte Carlo Method (MCMC), given small amount of test data. In this environment we show nice results using our approach. |
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| AbstractList | 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 test corpus based on Markov process Monte Carlo Method (MCMC), given small amount of test data. In this environment we show nice results using our approach. |
| Author | Miura, Takao Fukuda, Takuya |
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| Keywords | Word Segmentation Stochastic Techniques Markov Chain Monte Carlo (MCMC) method |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | A Stochastic Technique to Obtain Training Data for Word Segmentation |
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