This paper describes a fully automatic twostage machine learning architecture that learns temporal relations between pairs of events. The first stage learns the temporal attribut...
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
Methods for discovering causal knowledge from observational data have been a persistent topic of AI research for several decades. Essentially all of this work focuses on knowledge...
Marc Maier, Brian Taylor, Huseyin Oktay, David Jen...
Shortage of manually labeled data is an obstacle to supervised relation extraction methods. In this paper we investigate a graph based semi-supervised learning algorithm, a label ...
Jinxiu Chen, Dong-Hong Ji, Chew Lim Tan, Zheng-Yu ...
A challenging problem in open information extraction and text mining is the learning of the selectional restrictions of semantic relations. We propose a minimally supervised boots...