Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …

The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …

Coreference resolution is the task of determining linguistic expressions that refer to the same
real-world entity in natural language. Research on coreference resolution in the general …

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …

A fundamental challenge in developing high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

This paper proposes a unified approach to learning from constraints, which integrates the
ability of classical machine learning techniques to learn from continuous feature-based …

Standard inductive learning requires that training and test instances come from the same
distribution. Transfer learning seeks to remove this restriction. In shallow transfer, test …

Abstract Machine learning approaches to coreference resolution are typically supervised,
and require expensive labeled data. Some unsupervised approaches have been proposed …

Sentiment analysis is the problem of determining the polarity of a text with respect to a
particular topic. For most applications, however, it is not only necessary to derive the polarity …