Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Equip** 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 …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

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

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Abstract Entities, as the essential elements in relation extraction tasks, exhibit certain
structure. In this work, we formulate such entity structure as distinctive dependencies …

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict
the price movement in a daily basis, although the market impact of news can be absorbed …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Document-level relation extraction aims to detect the relations within one document, which is
challenging since it requires complex reasoning using mentions, entities, local and global …