Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Treebanks. In this paper, we investigate the e...
This paper presents the development of two related machine-learned models which predict (a) whether a student can answer correctly questions in an ILE without requesting help and (...
We present a discriminative, latent variable approach to syntactic parsing in which rules exist at multiple scales of refinement. The model is formally a latent variable CRF gramm...
We report on an on-going research project aimed at increasing the range of translation equivalents which can be automatically discovered by MT systems. The methodology is based on...
In this paper, we discuss how users and designers of existing learning management systems (LMSs) can make use of policies to enhance adaptivity and adaptability. Many widespread L...
Arne Wolf Koesling, Eelco Herder, Juri Luca De Coi...