Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

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 …

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 …

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 …

In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a
novel Chinese pre-trained unbalanced transformer (CPT). Different from previous Chinese …

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 …

Lexicon information and pre-trained models, such as BERT, have been combined to explore
Chinese sequence labelling tasks due to their respective strengths. However, existing …

Recently, word enhancement has become very popular for Chinese Named Entity
Recognition (NER), reducing segmentation errors and increasing the semantic and …

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

Named entity recognition (NER) is a fundamental task to recognize specific types of entities
from a given sentence. Depending on how the entities appear in the sentence, it can be …