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 …

With the rapid development of text mining, many studies observe that text generally contains
a variety of implicit information, and it is important to develop techniques for extracting such …

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

So far, named entity recognition (NER) has been involved with three major types, including
flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied …

Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Named Entity Recognition (NER) is the task of identifying spans that represent entities in
sentences. Whether the entity spans are nested or discontinuous, the NER task can be …

The task of named entity recognition (NER) is normally divided into nested NER and flat
NER depending on whether named entities are nested or not. Models are usually separately …

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing,
concerned with identifying spans of text expressing references to entities. NER research is …

Named entity recognition (NER) is a well-studied task in natural language processing.
Traditional NER research only deals with flat entities and ignores nested entities. The span …

Neural named entity recognition (NER) models may easily encounter the over-confidence
issue, which degrades the performance and calibration. Inspired by label smoothing and …