Abstract Zero-shot Information Extraction (IE) aims to build IE systems from the unannotated
text. This is a challenging task as it involves little human intervention, but it is also …

As a main field of artificial intelligence, natural language processing (NLP) has achieved
remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in …

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 …

Named entity recognition (NER) aims to identify the required entities and their types from
unstructured text, which can be utilized for the construction of knowledge graphs. Traditional …

Cross-lingual named entity recognition (CrossNER) faces challenges stemming from
uneven performance due to the scarcity of multilingual corpora, especially for non-English …

The objective of few-shot named entity recognition is to identify named entities with limited
labeled instances. Previous works have primarily focused on optimizing the traditional token …

Named entity recognition (NER) is the task to detect and classify the entity spans in the text.
When entity spans overlap between each other, this problem is named as nested NER …

One strategy to recognize nested entities is to enumerate overlapped entity spans for
classification. However, current models independently verify every entity span, which …

Purpose This study aims to introduce an innovative approach that uses a decoder with
multiple layers to accurately identify Chinese nested entities across various nesting depths …

Abstract Information Extraction (IE) spans several tasks with different output structures, such
as named entity recognition, relation extraction and event extraction. Previously, those tasks …