Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Pre-trained language models have attracted increasing attention in the biomedical domain,
inspired by their great success in the general natural language domain. Among the two main …

Information extraction suffers from its varying targets, heterogeneous structures, and
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …

Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Joint entity and relation extraction is an essential task in natural language processing and
knowledge graph construction. Existing approaches usually decompose the joint extraction …

Joint extraction of entities and relations from unstructured texts is a crucial task in information
extraction. Recent methods achieve considerable performance but still suffer from some …

Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Joint entity and relation extraction is an important task in natural language processing, which
aims to extract all relational triples mentioned in a given sentence. In essence, the relational …

Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …