We propose a class of Bayesian networks appropriate for structured prediction problems where the Bayesian network's model structure is a function of the predicted output stru...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
We address several challenges for applying statistical dialog managers based on Partially Observable Markov Models to real world problems: to deal with large numbers of concepts, ...
Sebastian Varges, Giuseppe Riccardi, Silvia Quarte...
A SAT-based incremental, inductive algorithm for model checking CTL properties is proposed. As in classic CTL model checking, the parse graph of the property shapes the analysis. H...
This paper describes an incremental approach to parsing transcribed spontaneous speech containing disfluencies with a Hierarchical Hidden Markov Model (HHMM). This model makes use...