Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Table 2: Aspect and opinion term extraction performance of different approaches. F1 score is reported. IHS_RD, DLIREC, Elixa and WDEmb* use manually designed features. For different versions of RINANTE, “Shared” and “Double” means shared BiLSTM model and double BiLSTM model, respectively; “Alt” and “Pre” means the first and the second training method, respectively. RINANTE-Double-Pre†: fine-tune the pre-trained model for only extracting aspect terms, and use the same number of validation samples as DE-CNN Xu et al. (2018). The results of RINANTE-Double-Pre† are obtained after this paper gets accepted and are not included in the ACL version. RINANTE-Double-Pre† achieves the best performance on SE15-R.
['Approach', 'SE14-R Aspect', 'SE14-R Opinion', 'SE14-L Aspect', 'SE14-L Opinion', 'SE15-R Aspect', 'SE15-R Opinion']
From the results, we can see that the mined rules alone do not perform well. However, by learning from the data automatically labeled by these rules, all four versions of RINANTE achieves better performances than RINANTE (no rule). This verifies that we can indeed use the results of the mined rules to improve the performance of neural models. Moreover, the improvement over RINANTE (no rule) can be especially significant on SE14-L and SE15-R. We think this is because SE14-L is relatively more difficult and SE15-R has much less manually labeled training data. This is because our algorithm is able to mine hundreds of effective rules, while Double Propagation only has eight manually designed rules.