# Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

arXiv: https://arxiv.org/abs/1601.03651 · HTML: https://mlsanity.com/paper/1601.03651/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2016.

## Built on by (most cited first, 26 of 26)

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- [Document-Level Relation Extraction with Sentences Importance Estimation and Focusing](https://mlsanity.com/paper/2204.12679/) (2022)
- [Learning Robust Representations for Continual Relation Extraction via Adversarial Class Augmentation](https://mlsanity.com/paper/2210.04497/) (2022)
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- [Influence-guided Data Augmentation for Neural Tensor Completion](https://mlsanity.com/paper/2108.10248/) (2021)
- [FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction](https://mlsanity.com/paper/2403.01886/) (2024)
- [Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling](https://mlsanity.com/paper/2211.05343/) (2022)
- [Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition](https://mlsanity.com/paper/2105.09045/) (2021)
- [TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2311.02355/) (2023)
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- [Data Augmentation for Machine Translation via Dependency Subtree Swapping](https://mlsanity.com/paper/2307.07025/) (2023)
- [A Global-Local Attention Mechanism for Relation Classification](https://mlsanity.com/paper/2407.01424/) (2024)
- [EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification](https://mlsanity.com/paper/2409.00908/) (2024)
- [Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences](https://mlsanity.com/paper/2409.13755/) (2024)
- [Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection](https://mlsanity.com/paper/2103.03602/) (2021)
- [STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction](https://mlsanity.com/paper/2209.01431/) (2022)
- [SegMix: A Simple Structure-Aware Data Augmentation Method](https://mlsanity.com/paper/2311.09505/) (2023)
- [Error-Aware Curriculum Learning for Biomedical Relation Classification](https://mlsanity.com/paper/2507.14374/) (2025)

## Abstract (arXiv)

Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architectures, researchers have improved the performance to a large extent in comparison with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolutional neural networks or recurrent networks). They may fail to explore the potential representation space in different abstraction levels. In this paper, we propose deep recurrent neural networks (DRNNs) for relation classification to tackle this challenge. Further, we propose a data augmentation method by leveraging the directionality of relations. We evaluated our DRNNs on the SemEval-2010 Task~8, and achieve an F1-score of 86.1%, outperforming previous state-of-the-art recorded results.