Logic and artificial intelligence
Artificial Intelligence
Journal of the ACM (JACM)
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning to reason the non monotonic case
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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Research in machine learning concentrates on the study of learning single concepts from examples. In this framework the learner attempts to learn a single hidden function from a collection of examples, assumed to be drawn independently from some unknown probability distribution. However, in many cases - as in most natural language and visual processing situations - decisions depend on the outcomes of several different but mutually dependent classifiers. The classifiers' outcomes need to respect some constraints that could arise from the sequential nature of the data or other domain specific conditions,thus requiring a level of inference on top the predictions.We will describe research and present challenges related to Inference with Classifiers - a paradigm in which we address the problem of using the outcomes of several different classifiers in making coherent inferences - those that respect constraints on the outcome of the classifiers. Examples will be given from the natural language domain.