Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Abstract Convolutional Neural Networks (CNNs) achieve impressive performance in a wide
variety of fields. Their success benefited from a massive boost when very deep CNN models …

We examine the capabilities of a unified, multi-task framework for three information
extraction tasks: named entity recognition, relation extraction, and event extraction. Our …

Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

To accelerate biomedical research process, deep-learning systems are developed to
automatically acquire knowledge about molecule entities by reading large-scale biomedical …

Dependency trees convey rich structural information that is proven useful for extracting
relations among entities in text. However, how to effectively make use of relevant information …

Multiple entities in a document generally exhibit complex inter-sentence relations, and
cannot be well handled by existing relation extraction (RE) methods that typically focus on …

Named entity recognition and relation extraction are two important fundamental problems.
Joint learning algorithms have been proposed to solve both tasks simultaneously, and many …

We introduce a multi-task setup of identifying and classifying entities, relations, and
coreference clusters in scientific articles. We create SciERC, a dataset that includes …