# Named Entity Recognition with stack residual LSTM and trainable bias decoding

arXiv: https://arxiv.org/abs/1706.07598 · HTML: https://mlsanity.com/paper/1706.07598/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2017.

## Built on by (most cited first, 5 of 5)

- [Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition](https://mlsanity.com/paper/2208.03054/) (2022)
- [Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2204.05518/) (2022)
- [Single versus Multiple Annotation for Named Entity Recognition of Mutations](https://mlsanity.com/paper/2101.07450/) (2021)
- [Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media](https://mlsanity.com/paper/2210.14163/) (2022)
- [Software Entity Recognition with Noise-Robust Learning](https://mlsanity.com/paper/2308.10564/) (2023)

## Abstract (arXiv)

Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.