Papers › Semantic Relation Classification via Convolutional Neural Networks with Simple...
Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling
Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao
Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset.
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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 | depLCNN + NS | F1 | 85.6 | #3 of 6 | Archive leaderboard | report |
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