In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of
prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot …

Unified Sequence Labeling that articulates different sequence labeling problems such as
Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a …

In the era of big data and artificial intelligence, healthcare data fusion analysis has become
difficult because of the large amounts and different types of sources involved. Traditional …

Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via
limited annotated samples. Recently, prototypical networks have shown promising …

Nested entities are prone to obtain similar representations in pre-trained language models,
posing challenges for Named Entity Recognition (NER), especially in the few-shot setting …

Abstract In Natural Language Processing (NLP), entities often contain other entities.
However, most current Named Entity Recognition (NER) methods can only recognize flat …

Chinese agricultural named entity recognition (NER) has been studied with supervised
learning for many years. However, considering the scarcity of public datasets in the …

Recognition of nested named entities, which may contain each other, can enhance the
coverage of found named entities. This capability is particularly useful for tasks such as …

Featured Application This study proposes a multiple fine-grained nested named entity
recognition model, which provides a solution for other specialized fields and lays the …

Few-shot Named Entity Recognition (NER) aims to extract entity information from limited
annotated samples, addressing the scarcity of data in specialized domains. However …