Communications of the ACM
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Statistical queries and faulty PAC oracles
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
The nature of statistical learning theory
The nature of statistical learning theory
Specification and simulation of statistical query algorithms for efficiency and noise tolerance
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Journal of the ACM (JACM)
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Linear concepts and hidden variables
Machine Learning
Automatic Rule Acquisition for Spelling Correction
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Relational Learning for NLP using Linear Threshold Elements
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Memory Based Learning in NLP
Learning in Natural Language
Memory-based learning: using similarity for smoothing
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A rule-based approach to prepositional phrase attachment disambiguation
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Pac-learning recursive logic programs: efficient algorithms
Journal of Artificial Intelligence Research
A mission for computational natural language learning
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
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This article summarizes work on developing a learning theory account for the major learning and statistics based approaches used in natural language processing. It shows that these approaches can all be explained using a single distribution free inductive principle related to the pac model of learning. Furthermore, they all make predictions using the same simple knowledge representation - a linear representation over a common feature space. This is significant both to explaining the generalization and robustness properties of these methods and to understanding how these methods might be extended to learn from more structured, knowledge intensive examples, as part of a learning centered approach to higher level natural language inferences.