# A Boundary Regression Model for Nested Named Entity Recognition

arXiv: https://arxiv.org/abs/2011.14330 · HTML: https://mlsanity.com/paper/2011.14330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses](https://mlsanity.com/paper/2405.00291/) (2024)
- [SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2210.05632/) (2022)

## Abstract (arXiv)

Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for a word or a NE candidate in a sentence. In shallow structures, categorized features are weighted to support the prediction. Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations. This approach unfolds a dense space saturated with high-order abstract semantic information, where the prediction is based on distributed feature representations. In this paper, positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework, which simultaneously predicts the classification score of a NE candidate and refine its spatial location in a sentence. It has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sntence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability. Experiments demonstrate state-of-the-art performance for nested NE recognitionfootnoteOur codes to implement the BR model are available at: urlhttps://github.com/wuyuefei3/BR..