Papers › Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling
Zhifang Fan, Zhen Wu, Xin-yu Dai, Shu-Jian Huang, Jia-Jun Chen
Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target. A target-fused sequence labeling neural network model is designed to perform this task. The opinion target information is well encoded into context by an Inward-Outward LSTM. Then left and right contexts of the opinion target and the global context are combined to find the corresponding opinion words. We build four datasets for TOWE based on several popular ABSA benchmarks from laptop and restaurant reviews. The experimental results show that our proposed model outperforms the other compared methods significantly. We believe that our work may not only be helpful for downstream sentiment analysis task, but can also be used for pair-wise opinion summarization.
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 | IOG | Laptop 2014 (F1) | 71.35 | #5 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | IOG | Restaurant 2014 (F1) | 80.02 | #5 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | IOG | Restaurant 2015 (F1) | 73.25 | #5 of 5 | Archive leaderboard | report |
| Aspect-oriented Opinion Extraction | SemEval-2014 Task-4 | IOG | Restaurant 2016 (F1) | 81.69 | #5 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.
Methods
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