Papers › A Dependency-Based Neural Network for Relation Classification
A Dependency-Based Neural Network for Relation Classification
Yang Liu, Furu Wei, Sujian Li, Heng Ji, Ming Zhou, Houfeng Wang
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Relation Classification | SemEval 2010 Task 8 | DepNN | F1 | 83.6 | #5 of 6 | Archive leaderboard | report |
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