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 …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

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 …

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) is a more challenging task compared to its
sentence-level counterpart. It aims to extract relations from multiple sentences at once. In …

As a core task and an important link in the fields of natural language understanding and
information retrieval, information extraction (IE) can structure and semanticize unstructured …

Pre-trained language models (PLMs) have gained increasing popularity due to their
compelling prediction performance in diverse natural language processing (NLP) tasks …

Sentence-level relation extraction (RE) aims at identifying the relationship between two
entities in a sentence. Many efforts have been devoted to this problem, while the best …

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 …