Papers › Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Zhen Wu, Fei Zhao, Xin-yu Dai, Shu-Jian Huang, Jia-Jun Chen
Target-oriented opinion words extraction (TOWE) is a new subtask of ABSA, which aims to extract the corresponding opinion words for a given opinion target in a sentence. Recently, neural network methods have been applied to this task and achieve promising results. However, the difficulty of annotation causes the datasets of TOWE to be insufficient, which heavily limits the performance of neural models. By contrast, abundant review sentiment classification data are easily available at online review sites. These reviews contain substantial latent opinions information and semantic patterns. In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE. To address the challenges in the transfer process, we design an effective transformation method to obtain latent opinions, then integrate them into TOWE. Extensive experimental results show that our model achieves better performance compared to other state-of-the-art methods and significantly outperforms the base model without transferring opinions knowledge. Further analysis validates the effectiveness of our model.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | LOTN | Laptop 2014 (F1) | 72.02 | #4 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | LOTN | Restaurant 2014 (F1) | 82.21 | #4 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | LOTN | Restaurant 2015 (F1) | 73.29 | #4 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | LOTN | Restaurant 2016 (F1) | 83.62 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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