# Combining Recurrent and Convolutional Neural Networks for Relation Classification

arXiv: https://arxiv.org/abs/1605.07333 · HTML: https://mlsanity.com/paper/1605.07333/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2016.

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

- [It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers](https://mlsanity.com/paper/2204.11424/) (2022)
- [Modeling Multi-Granularity Hierarchical Features for Relation Extraction](https://mlsanity.com/paper/2204.04437/) (2022)
- [Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU](https://mlsanity.com/paper/2203.17079/) (2022)
- [A Sequence Tagging based Framework for Few-Shot Relation Extraction](https://mlsanity.com/paper/2208.08053/) (2022)
- [Relation Extraction with Instance-Adapted Predicate Descriptions](https://mlsanity.com/paper/2503.17799/) (2025)

## Abstract (arXiv)

This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended middle context). Furthermore, we propose connectionist bi-directional recurrent neural networks and introduce ranking loss for their optimization. Finally, we show that combining convolutional and recurrent neural networks using a simple voting scheme is accurate enough to improve results. Our neural models achieve state-of-the-art results on the SemEval 2010 relation classification task.