# Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

arXiv: https://arxiv.org/abs/1707.07719 · HTML: https://mlsanity.com/paper/1707.07719/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2017.

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

- [Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction](https://mlsanity.com/paper/2103.11062/) (2021)
- [Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Models](https://mlsanity.com/paper/2310.11085/) (2023)
- [A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2106.14163/) (2021)
- [CEntRE: A paragraph-level Chinese dataset for Relation Extraction among Enterprises](https://mlsanity.com/paper/2210.10581/) (2022)

## Abstract (arXiv)

We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.