In this paper, we propose DiffusionNER, which formulates the named entity recognition task
as a boundary-denoising diffusion process and thus generates named entities from noisy …

We consider the end-to-end abstract-to-title generation problem, exploring seven recent
transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title …

The automatic extraction of biomedical entities and their interaction from unstructured data
remains a challenging task due to the limited availability of expert-labeled standard …

Relation extraction is an important task in the field of natural language processing. Previous
works mainly focus on adopting pipeline methods or joint methods to model relation …

By modeling the interaction among instances and avoiding error propagation, Set Prediction
Networks (SPNs) achieve state-of-the-art performance on the tasks of named entity …

Relational triple extraction is crucial work for the automatic construction of knowledge
graphs. Existing methods only construct shallow representations from a token or token pair …

Abstract Named Entity Recognition (NER) is a critical component in the construction of
knowledge graphs. However, traditional approaches often neglect the discrepancy between …

Knowledge extraction is a significant issue in fields including knowledge graph construction
and natural language processing. Triple extraction, as a crucial issue within knowledge …