# Graph Convolutional Networks for Named Entity Recognition

arXiv: https://arxiv.org/abs/1709.10053 · HTML: https://mlsanity.com/paper/1709.10053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 4 of 4)

- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [Named entity recognition architecture combining contextual and global features](https://mlsanity.com/paper/2112.08033/) (2021)
- [Graph Convolutional Network for Swahili News Classification](https://mlsanity.com/paper/2103.09325/) (2021)
- [An Encoder-Integrated PhoBERT with Graph Attention for Vietnamese Token-Level Classification](https://mlsanity.com/paper/2510.11537/) (2025)

## Abstract (arXiv)

In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of GCN. We perform a comparison among different NER architectures and show that the grammar of a sentence positively influences the results. Experiments on the ontonotes dataset demonstrate consistent performance improvements, without requiring heavy feature engineering nor additional language-specific knowledge.