Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Bias in textual data can lead to skewed interpretations and outcomes when the data is used.
These biases could perpetuate stereotypes, discrimination, or other forms of unfair …

Recent advances in Named Entity Recognition (NER) show that document-level contexts
can significantly improve model performance. In many application scenarios, however, such …

The pre-training of text encoders normally processes text as a sequence of tokens
corresponding to small text units, such as word pieces in English and characters in Chinese …

Recently, Flat-LAttice Transformer (FLAT) has achieved great success in Chinese Named
Entity Recognition (NER). FLAT performs lexical enhancement by constructing flat lattices …

Named entity recognition (NER) isa preliminary task in natural language processing (NLP).
Recognizing Chinese named entities from unstructured texts is challenging due to the lack …

With social media posts tending to be multimodal, Multimodal Named Entity Recognition
(MNER) for the text with its accompanying image is attracting more and more attention since …

As an important foundational task in the field of natural language processing, the Chinese
named entity recognition (NER) task has received widespread attention in recent years. Self …

Recently, the revival of the semantic similarity concept has been featured by the rapidly
growing artificial intelligence research fueled by advanced deep learning architectures …

NER model has achieved promising performance on standard NER benchmarks. However,
recent studies show that previous approaches may over-rely on entity mention information …