Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …

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

We introduce SpERT, an attention model for span-based joint entity and relation extraction.
Our key contribution is a light-weight reasoning on BERT embeddings, which features entity …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

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 …

Document-level relation extraction (RE) poses new challenges compared to its sentence-
level counterpart. One document commonly contains multiple entity pairs, and one entity pair …

Document-level relation extraction aims to extract relations among entities within a
document. Different from sentence-level relation extraction, it requires reasoning over …