Abstract Named Entity Recognition (NER), one of the most fundamental problems in natural
language processing, seeks to identify the boundaries and types of entities with specific …

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 …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

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 …

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 …

Multi-modal named entity recognition (MNER) aims to discover named entities in free text
and classify them into pre-defined types with images. However, dominant MNER models do …

Span-based methods with the neural networks backbone have great potential for the nested
named entity recognition (NER) problem. However, they face problems such as …

Named entity recognition (NER) is a fundamental task in natural language processing.
Recent works treat named entity recognition as a reading comprehension task, constructing …

Named entity recognition (NER) is a widely studied task in natural language processing.
Recently, a growing number of studies have focused on the nested NER. The span-based …

Event extraction (EE) is a crucial information extraction task that aims to extract event
information in texts. Most existing methods assume that events appear in sentences without …