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 …

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 …

Multimodal named entity recognition and relation extraction (MNER and MRE) is a
fundamental and crucial branch in information extraction. However, existing approaches for …

Triple extraction is an essential task in information extraction for natural language
processing and knowledge graph construction. In this paper, we revisit the end-to-end triple …

We present an open-source and extensible knowledge extraction toolkit DeepKE,
supporting complicated low-resource, document-level and multimodal scenarios in the …

Information Extraction (IE) aims to extract structural information from unstructured texts. In
practice, long-tailed distributions caused by the selection bias of a dataset, may lead to …

Business Knowledge Graphs (KGs) are important to many enterprises today, providing
factual knowledge and structured data that steer many products and make them more …

Information extraction tasks such as entity relation extraction and event extraction are of
great importance for natural language processing and knowledge graph construction. In this …

Event understanding aims at understanding the content and relationship of events within
texts, which covers multiple complicated information extraction tasks: event detection, event …

Extraction of Application Programming Interfaces (APIs) and their semantic relations from
unstructured text (eg, Stack Overflow) is a fundamental work for software engineering tasks …