A Comprehensive Survey of Document-level Relation Extraction (2016-2023) (2309.16396v3)
Abstract: Document-level relation extraction (DocRE) is an active area of research in NLP concerned with identifying and extracting relationships between entities beyond sentence boundaries. Compared to the more traditional sentence-level relation extraction, DocRE provides a broader context for analysis and is more challenging because it involves identifying relationships that may span multiple sentences or paragraphs. This task has gained increased interest as a viable solution to build and populate knowledge bases automatically from unstructured large-scale documents (e.g., scientific papers, legal contracts, or news articles), in order to have a better understanding of relationships between entities. This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction.
- Julien Delaunay (8 papers)
- Hanh Thi Hong Tran (5 papers)
- Carlos-Emiliano González-Gallardo (11 papers)
- Georgeta Bordea (3 papers)
- Nicolas Sidere (4 papers)
- Antoine Doucet (18 papers)