Introduction to the special issue on learning semantics

The 2014 Special Issue of Machine Learning discusses several papers on learning semantics. The first paper of the special issue, 'From Machine Learning to Machine Reasoning' by Léon Bottou is an essay which attempts to bridge trainable systems, like neural networks, and sophisticated 'all-purpose' inference mechanisms, such as logical or probabilistic inference. The paper 'Learning Perceptually Grounded Word Meanings from Unaligned Parallel Data' by Stefanie Tellex, Pratiksha Thaker, Joshua Joseph and Nicholas Roy describes an approach to map natural language commands to actions for a forklift control task. The paper 'Interactive Relational Reinforcement Learning of Concept Semantics' by Matthias Nickles and Achim Rettinger presents a Relational Reinforcement Learning (RRL) approach for learning denotational concept semantics using symbolic interaction of artificial agents with human users.