# Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

arXiv: https://arxiv.org/abs/1812.11321 · HTML: https://mlsanity.com/paper/1812.11321/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 10 of 10)

- [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://mlsanity.com/paper/2104.07650/) (2021)
- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Efficient-CapsNet: Capsule Network with Self-Attention Routing](https://mlsanity.com/paper/2101.12491/) (2021)
- [Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion](https://mlsanity.com/paper/2106.08657/) (2021)
- [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022)
- [Deep Neural Network Based Relation Extraction: An Overview](https://mlsanity.com/paper/2101.01907/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Depth as Attention for Face Representation Learning](https://mlsanity.com/paper/2101.00652/) (2021)
- [Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction](https://mlsanity.com/paper/2104.00312/) (2021)
- [IBCapsNet: Information Bottleneck Capsule Network for Noise-Robust Representation Learning](https://mlsanity.com/paper/2603.20682/) (2026)

## Abstract (arXiv)

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.