Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis
Table 3: Stage one results of aspect extraction and sentiment classification. (All models were trained in the unified tag setting.)
['[EMPTY]', '14res P', '14res R', '14res F', '14lap P', '14lap R', '14lap F', '15res P', '15res R', '15res F', '16res P', '16res R', '16res F']
Our model outperforms existing strong baselines (i.e. RINANTE, CMLA, and Li-Unified) on all datasets, especially compared with the Li-unified model which is the state-of-the-art in the unified task. Interestingly, the baseline Li-unified-R, derived from Li-unified, performs very competitive, i.e. better than Li-unified on all datasets. It shows that given the ground-truth label of opinion words, explicitly modeling opinion extraction can help upgrade the performance of aspect extraction. We also notice that Li-unified-R outperforms our full model on 14res and 14lap. Another insight is that the performance of RINANTE and CMLA reduced a lot in the unified tag setting, comparing with their original setting. We believe this is due to the lack of specific design to utilize sentiment information. Thus, for reference, we evaluate them under their original setting, i.e. only considering the target boundary and ignoring the sentiment polarity. The results shown in the last two rows increase drastically compared with those in the unified tag setting.