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...
This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional proba...
Andrew McCallum, Chris Pal, Gregory Druck, Xuerui ...
This article describes a dispensation order generation algorithm for genotyping using the Pyrosequencing method. The input template of the algorithm is a slightly restricted regul...
We present a hierarchical phrase-based statistical machine translation in which a target sentence is efficiently generated in left-to-right order. The model is a class of synchron...
This paper presents the conception, the design and a prototype of a virtual reality system for learning and maintenance training of Hydroelectric Generating Unit (HGU). The system...