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A fully Bayesian approach to unsupervised part-of-speech tagging
Sharon Goldwater
,
Thomas L. Griffiths
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
239
Scopus citations
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Dive into the research topics of 'A fully Bayesian approach to unsupervised part-of-speech tagging'. Together they form a unique fingerprint.
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Keyphrases
Maximum Likelihood Estimation
100%
Speech Tagging
100%
Part-of
100%
Fully Bayesian Approach
100%
High Probability
50%
Performance Improvement
50%
Unsupervised Learning
50%
Difficult Problem
50%
Natural Language
50%
Bayesian Approach
50%
Generative Models
50%
Parameter Values
50%
Dictionary
50%
Hidden Structure
50%
Sparse Distribution
50%
Discriminative Model
50%
Linguistic Structure
50%
Trigram
50%
Computer Science
Bayesian Approach
100%
Parts Of Speech Tagging
100%
maximum-likelihood
66%
Likelihood Estimation
66%
Unsupervised Learning
33%
Parameter Value
33%
Generative Model
33%
Hidden Structure
33%
Mathematics
Bayesian Approach
100%
Probability Theory
66%
Maximum Likelihood Estimation
66%
Observed Data
33%
Percentage Point
33%
Engineering
Bayesian Approach
100%
Maximum Likelihood Estimation
66%
Model Parameter
33%
Observed Data
33%
Generative Model
33%
Earth and Planetary Sciences
Marking
100%
State of the Art
33%
Economics, Econometrics and Finance
Bayesian
100%