# A Dependency-Based Neural Network for Relation Classification

arXiv: https://arxiv.org/abs/1507.04646 · HTML: https://mlsanity.com/paper/1507.04646/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2015.

## Built on by (most cited first, 7 of 7)

- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [Unified Text Structuralization with Instruction-tuned Language Models](https://mlsanity.com/paper/2303.14956/) (2023)
- [Event-based clinical findings extraction from radiology reports with pre-trained language model](https://mlsanity.com/paper/2112.13512/) (2021)
- [Silver Syntax Pre-training for Cross-Domain Relation Extraction](https://mlsanity.com/paper/2305.11016/) (2023)
- [Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences](https://mlsanity.com/paper/2409.13755/) (2024)
- [Structural block driven - enhanced convolutional neural representation for relation extraction](https://mlsanity.com/paper/2103.11356/) (2021)
- [TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction](https://mlsanity.com/paper/2303.17119/) (2023)

## Abstract (arXiv)

Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.