We present a novel approach for the automatic generation of model-to-model transformations given a description of the operational semantics of the source language in the form of gr...
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and the...
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee...
We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, r...
This study experimentally manipulates common ground (the knowledge, beliefs and assumptions interlocutors mutually share [6]) and measures the effect on speakers' use of inter...
An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering techniq...