DiffusionNER: Boundary Diffusion for Named Entity Recognition

TL;DR

DiffusionNER introduces a boundary-denoising diffusion approach for named entity recognition, enabling flexible and efficient entity generation by progressively refining noisy span boundaries through learned reverse diffusion.

Contribution

It formulates NER as a boundary diffusion process, a novel approach that improves entity recognition by dynamic boundary refinement during generation.

Findings

Achieves state-of-the-art or comparable performance on multiple NER datasets.

Demonstrates effective boundary refinement through diffusion process.

Provides a flexible framework for both flat and nested NER tasks.

Abstract

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 spans. During training, DiffusionNER gradually adds noises to the golden entity boundaries by a fixed forward diffusion process and learns a reverse diffusion process to recover the entity boundaries. In inference, DiffusionNER first randomly samples some noisy spans from a standard Gaussian distribution and then generates the named entities by denoising them with the learned reverse diffusion process. The proposed boundary-denoising diffusion process allows progressive refinement and dynamic sampling of entities, empowering DiffusionNER with efficient and flexible entity generation capability. Experiments on multiple flat and nested NER datasets demonstrate that DiffusionNER achieves comparable or even better performance than previous state-of-the-art models.