As a means of modeling typical system behavior, we present a description technique for business processes derived from data flow nets and provide it with a formal semantics based ...
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
This paper reports on the benefits of largescale statistical language modeling in machine translation. A distributed infrastructure is proposed which we use to train on up to 2 t...
Thorsten Brants, Ashok C. Popat, Peng Xu, Franz Jo...
In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect traini...
Abstract. We propose a learning approach for integrating formal knowledge into statistical inference by exploiting ontologies as a semantically rich and fully formal representation...