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 …

Tremendous progress has been made in the development of fine-tuned pretrained language
models (PLMs) that achieve outstanding results on almost all natural language processing …

Document-level relation extraction (DocRE) aims to infer complex semantic relations among
entities in a document. Distant supervision (DS) is able to generate massive auto-labeled …

Document-level relation extraction (DocRE) is an active area of research in natural
language processing (NLP) concerned with identifying and extracting relationships between …

Extracting relations from a document is more challenging than from a sentence, due to the
involvement of more entities and complex contextual information. To capture long-distance …

Knowledge Bases (KBs) have become more complex because some facts in KBs include
more than two entities. The construction and completion of these KBs require a new relation …

Document-level relation extraction (RE) aims to predict the relational facts between two
given entities from a document. Unlike widespread research on document-level RE in …

Document-level relation extraction extracts relations between entity pairs from multiple
sentences in a document by reasoning about semantic relationships between entities and …

Document-level relation extraction aims to uncover relations between entities by harnessing
the intricate information spread throughout a document. Previous research involved …

The entity-relationship extraction model has a significant influence in relation extraction. The
existing model cannot effectively identify the entity-relationship triples in overlapping …