Document-level Relation Triplet Extraction (DocRTE) is a fundamental task in information
systems that aims to simultaneously extract entities with semantic relations from a document …

How to identify semantic relations among entities in a document when only a few labeled
documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial …

This comprehensive survey delves into the latest advancements in Relation Extraction (RE),
a pivotal task in natural language processing essential for applications across biomedical …

Document-level interaction extraction for Chemical-Disease is aimed at inferring the
interaction relations between chemical entities and disease entities across multiple …

Abstract Information Extraction (IE), aiming to extract structured information from
unstructured natural language texts, can significantly benefit from pre-trained language …

Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs)
to specific domains or tasks. However, as demonstrated by empirical experiments, the …

Relation extraction (RE) extracts structured tuples of relationships (eg friend, enemy)
between entities (eg Sherlock Holmes, John Watson) from text, with exciting potential …

Continual relation extraction aims to learn new relations from a continuous stream of data
while avoiding forgetting old relations. Existing methods typically use the BERT encoder to …

Despite its popularity in sentence-level relation extraction, distantly supervised data is rarely
utilized by existing work in document-level relation extraction due to its noisy nature and low …

Document-level relation extraction (DRE) aims to extract relations between entities in
unstructured documents. Unlike sentence-level relation extraction, DRE introduces …