Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction
Table 4: Main experiment results(%). Best results are in bold (P, R, and F1-score, the larger is the better). The marker† represents that LOTN outperforms other methods significantly (p<0.01) .
['Models', '14res P', '14res R', '14res F1', '14lap P', '14lap R', '14lap F1', '15res P', '15res R', '15res F1', '16res P', '16res R', '16res F1']
We can observe that pure rule-based methods perform very poorly compared to the supervised learning models. The method Distance-rule achieves the worst recall and F1-score in most of the datasets since it only detects the single word as opinion word. Dependency-rule method obtains some improvements, but it is still worse than the sequence labeling models.