We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus mean...
In this paper, we propose a novel method for semi-supervised learning of nonprojective log-linear dependency parsers using directly expressed linguistic prior knowledge (e.g. a no...
We propose a low cost method for the correction of the output of OCR engines through the use of human labor. The method employs an error estimator neural network that learns to as...
Machine learning, data mining, and several related research areas are concerned with methods for the automated induction of models and the extraction of interesting patterns from ...
Abstract. We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated acc...
Paolo Viappiani, Sandra Zilles, Howard J. Hamilton...