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

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 …

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 …

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

Computer-aided process planning is the bridge between computer-aided design and
computer-aided manufacturing. With the advent of the intelligent manufacturing era, process …

The challenge of information extraction (IE) lies in the diversity of label schemas and the
heterogeneity of structures. Traditional methods require task-specific model design and rely …

Aspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a
sentence. Recent generative methods such as Seq2Seq models have achieved good …

Motivated by the fact that many relations cross the sentence boundary, there has been
increasing interest in document-level relation extraction (DocRE). DocRE requires …

Table filling based relational triple extraction methods are attracting growing research
interests due to their promising performance and their abilities on extracting triples from …