Papers › Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a...
Don't Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction
Zhenkai Wei, Yu Hong, Bowei Zou, Meng Cheng, Jianmin Yao
The current aspect extraction methods suffer from boundary errors. In general, these errors lead to a relatively minor difference between the extracted aspects and the ground-truth. However, they hurt the performance severely. In this paper, we propose to utilize a pointer network for repositioning the boundaries. Recycling mechanism is used, which enables the training data to be collected without manual intervention. We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant. Experimental results show that our method achieves substantial improvements over the baseline, and outperforms state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect Extraction | SemEval 2015 Task 12 | Wei et al. (2020) | Restaurant (F1) | 72.7 | #2 of 2 | Archive leaderboard | report |
| Aspect Extraction | SemEval-2014 Task-4 | Wei et al. (2020) | Laptop (F1) | 82.7 | #5 of 6 | Archive leaderboard | report |
| Aspect Extraction | SemEval-2014 Task-4 | Wei et al. (2020) | Restaurant (F1) | 87.1 | #5 of 6 | Archive leaderboard | report |
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Methods
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