A maximum entropy approach to natural language processing
Computational Linguistics
Stochastic attribute-value grammars
Computational Linguistics
A hybrid approach for named entity and sub-type tagging
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Feature lattices for maximum entropy modelling
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Maximum entropy model learning of the translation rules
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Markov random field based English part-of-speech tagging system
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
ACM Transactions on Asian Language Information Processing (TALIP)
Improvement of a Whole Sentence Maximum Entropy Language Model using grammatical features
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Question answering using maximum entropy components
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatic derivation of surface text patterns for a maximum entropy based question answering system
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
A maximum entropy/minimum divergence translation model
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information
Computational Linguistics
Incorporating position information into a Maximum Entropy/Minimum Divergence translation model
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Overfitting avoidance for stochastic modeling of attribute-value grammars
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Using perfect sampling in parameter estimation of a whole sentence maximum entropy language model
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Statistical QA - classifier vs. re-ranker: what's the difference?
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
A DOM tree alignment model for mining parallel data from the web
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A maximum entropy word aligner for Arabic-English machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Reduction of maximum entropy models to hidden markov models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the field and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random field models and techniques introduced in this paper differ from those common to much of the computer vision literature in that the underlying random fields are non-Markovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classification in natural language processing.