A Comprehensive Survey of Document-level Relation Extraction (2016-2023)

Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano González-Gallardo, Georgeta Bordea, Nicolas Sidere +1 more
9/28/2023
cs.CL

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Abstract

Document-level relation extraction (DocRE) is an active area of research in natural language processing (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.

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Cite this paper

@article{delaunay2023comprehensive,
  title  = {A Comprehensive Survey of Document-level Relation Extraction (2016-2023)},
  author = {Julien Delaunay and Hanh Thi Hong Tran and Carlos-Emiliano González-Gallardo and Georgeta Bordea and Nicolas Sidere and Antoine Doucet},
  year   = {2023},
  eprint = {2309.16396},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.16396v3}
}

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