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

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 extract relations among multiple entity pairs from
a document. Previously proposed graph-based or transformer-based models utilize the …

Structure information extraction refers to the task of extracting structured text fields from web
pages, such as extracting a product offer from a shopping page including product title …

Document-level relation extraction (DocRE) is the task of identifying all relations between
each entity pair in a document. Evidence, defined as sentences containing clues for the …

Document-level relation extraction (DocRE) aims to extract semantic relations among entity
pairs in a document. Typical DocRE methods blindly take the full document as input, while a …

We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …

Stepping from sentence-level to document-level, the research on relation extraction (RE)
confronts increasing text length and more complicated entity interactions. Consequently, it is …

Document-level relation extraction (DocRE) models generally use graph networks to
implicitly model the reasoning skill (ie, pattern recognition, logical reasoning, coreference …

Document-level relation extraction (RE) aims to extract relational triples from a document.
One of its primary challenges is to predict implicit relations between entities, which are not …