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145,652
Chunking with Maximum Entropy Models
, 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 ..."
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Cited by 33 (1 self)
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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
, 2006
"... Phonebased 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 phonebased approach should enable it to achieve higher levels of accuracy than spec ..."
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present a discriminative approach to phonebased language recognition based on conditional maximum entropy models. We present results from our preliminary experiments with two types of backend classifiers on a tenlanguage task using short files of varying durations from the handsegmented and labeled
A Maximum Entropy Model For Parsing
 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 ..."
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Cited by 29 (2 self)
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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
Mixtures of Conditional Maximum Entropy Models
 In Proc. of ICML2003
, 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 ..."
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Cited by 14 (8 self)
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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
How biased are maximum entropy models
 Advances in neural information processing
, 2011
"... Maximum entropy models have become popular statistical models in neuroscience and other areas in biology, and can be useful tools for obtaining estimates of mutual information in biological systems. However, maximum entropy models fit to small data sets can be subject to sampling bias; i.e. the tru ..."
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Cited by 2 (0 self)
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Maximum entropy models have become popular statistical models in neuroscience and other areas in biology, and can be useful tools for obtaining estimates of mutual information in biological systems. However, maximum entropy models fit to small data sets can be subject to sampling bias; i
Maximum Entropy Models for Skin Detection
 In Proceedings Third Indian Conference on Computer Vision, Graphics and Image Processing
, 2002
"... We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from pract ..."
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Cited by 13 (2 self)
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We consider a sequence of three models for skin detection built from a large collection of labelled images. Each model is a maximum entropy model with respect to constraints concerning marginal distributions. Our models are nested. The first model, called the baseline model is well known from
Maximum Entropy Models for Realization Ranking
 In Proceedings of the 10th Machine Translation Summit (pp. 109
, 2005
"... In this paper we describe and evaluate di#erent statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input semantics. Three models are trained and tested; an ngram language model, a discriminative max ..."
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Cited by 20 (1 self)
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maximum entropy model using structural features, and a combination of these two. Our realization component forms part of a larger, hybrid MT system.
Feature Lattices for Maximum Entropy Modelling
 In Proc. of ACLCOLING
, 1998
"... Maximum entropy framework proved to be expressive and powerful for the statistical lan guage modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the paxameter estimation is computationally expensive while the feature ..."
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Maximum entropy framework proved to be expressive and powerful for the statistical lan guage modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the paxameter estimation is computationally expensive while
Maximum Entropy Modeling with Clausal Constraints
 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. ..."
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Cited by 37 (1 self)
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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
Feature Lattices for Maximum Entropy Modelling
 In Proc. of ACLCOLING
, 1998
"... Maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the parameter estimation is computationally expensive while the feat ..."
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Cited by 37 (5 self)
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Maximum entropy framework proved to be expressive and powerful for the statistical language modelling, but it suffers from the computational expensiveness of the model building. The iterative scaling algorithm that is used for the parameter estimation is computationally expensive while
Results 1  10
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145,652