Results 1 - 10 of 47,407

Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

Discriminative Training and Maximum Entropy Models for Statistical Machine Translation

by Franz Josef Och, Hermann Ney , 2002
"... We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language senten ..."
Abstract - Cited by 508 (30 self) - Add to MetaCart
We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language

A Maximum Entropy Model for Part-Of-Speech Tagging

by Adwait Ratnaparkhi , 1996
"... This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
Abstract - Cited by 580 (1 self) - Add to MetaCart
This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "

Chunking with Maximum Entropy Models

by Rob Koeling , 2000
"... this paper I discuss a first attempt to create a text chunker using a Maximum Entropy model. The first experiments, implementing classifiers that tag every word in a sentence with a phrasetag using very local lexical information, partof -speech tags and phrase tags of surrounding words, give encoura ..."
Abstract - Cited by 33 (1 self) - Add to MetaCart
this paper I discuss a first attempt to create a text chunker using a Maximum Entropy model. The first experiments, implementing classifiers that tag every word in a sentence with a phrasetag using very local lexical information, partof -speech tags and phrase tags of surrounding words, give

with Maximum Entropy Models

by Robbie A. Haertel, Deryle Lonsdale, Eric K. Ringger, Nathan Ekstrom, Robbie A. Haertel, Deryle Lonsdale, Eric K. Ringger, Nathan Ekstrom , 2006
"... Phone-based automatic language recognition systems have recently been surpassed in accuracy by systems that make use of spectral information alone. Intuitively, it seems that the higher level knowledge employed by a phone-based approach should enable it to achieve higher levels of accuracy than spec ..."
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present a discriminative approach to phone-based language recognition based on conditional maximum entropy models. We present results from our preliminary experiments with two types of back-end classifiers on a ten-language task using short files of varying durations from the handsegmented and labeled

A Maximum Entropy Model For Parsing

by Adwait Ratnaparkhi, Salim Roukos, R. Todd Ward - In Proceedings of the International Conference on Spoken Language Processing
"... this paper, we present a method where more of the tree structure is used in the parsing model. We define a set of features that capture long distance dependency such as parallelism in coordination. These features are then integrated with a Maximum Entropy model into an overall probabilistic model fo ..."
Abstract - Cited by 27 (2 self) - Add to MetaCart
this paper, we present a method where more of the tree structure is used in the parsing model. We define a set of features that capture long distance dependency such as parallelism in coordination. These features are then integrated with a Maximum Entropy model into an overall probabilistic model

A Gaussian prior for smoothing maximum entropy models

by Stanley F. Chen, Ronald Rosenfeld , 1999
"... In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood train-ing for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem ..."
Abstract - Cited by 253 (2 self) - Add to MetaCart
In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood train-ing for exponential models, and like other maximum likelihood methods is prone to overfitting of training data. Several smoothing methods for maximum entropy models have been proposed to address

Mixtures of Conditional Maximum Entropy Models

by Dmitry Pavlov, Alexandrin Popescul, David M. Pennock, Lyle H. Ungar - In Proc. of ICML-2003 , 2002
"... Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation problems to better handle the case where complex data distributions arise from a mixture of simpler u ..."
Abstract - Cited by 14 (8 self) - Add to MetaCart
Driven by successes in several application areas, maximum entropy modeling has recently gained considerable popularity. We generalize the standard maximum entropy formulation of classi cation problems to better handle the case where complex data distributions arise from a mixture of simpler

Feature Lattices for Maximum Entropy Modelling

by Andrei Mikheev - In Proc. of ACL-COLING , 1998
"... Maximum entropy framework proved to be ex-pressive and powerful for the statistical lan-guage modelling, but it suffers from the com-putational expensiveness of the model build-ing. The iterative scaling algorithm that is used for the parameter estimation is computation-ally expensive while the feat ..."
Abstract - Cited by 37 (5 self) - Add to MetaCart
Maximum entropy framework proved to be ex-pressive and powerful for the statistical lan-guage modelling, but it suffers from the com-putational expensiveness of the model build-ing. The iterative scaling algorithm that is used for the parameter estimation is computation-ally expensive while

Maximum Entropy Modeling with Clausal Constraints

by Luc Dehaspe - In Proceedings of the 7th International Workshop on Inductive Logic Programming , 1997
"... We present the learning system Maccent which addresses the novel task of stochastic MAximum ENTropy modeling with Clausal Constraints. Maximum Entropy method is a Bayesian method based on the principle that the target stochastic model should be as uniform as possible, subject to known constraints. ..."
Abstract - Cited by 36 (1 self) - Add to MetaCart
We present the learning system Maccent which addresses the novel task of stochastic MAximum ENTropy modeling with Clausal Constraints. Maximum Entropy method is a Bayesian method based on the principle that the target stochastic model should be as uniform as possible, subject to known constraints
Results 1 - 10 of 47,407