Enhancing Review Comprehension with Domain-Specific Commonsense
Table 6: Aspect Extraction (AE, left) and Aspect Sentiment Classification (ASC, right) results. The BERT-PT numbers are taken from Xu et al. (2019). P: precision, R: recall, MF1: Macro-F1. The standard deviation is 1.51 for AE and 1.06 for ASC.
['[EMPTY]', 'Restaurant Acc', 'Restaurant MF1', 'Laptop Acc', 'Laptop MF1']
ABSA results. We measure the model performance on AE tasks using F1 and the model performance on ASC tasks using both accuracy and macro-F1. xSense consistently outperforms BERT-PT (SOTA) on all datasets. The improvements range from 0.18 (F1 for Laptop AE) to 3.18 (MF1 for Restaurant ASC). The improvement is higher for the restaurant domain. Intuitively, this is because the restaurant KB is of better quality. Moreover, we notice that ConceptNet hurts the BERT performance on most cases while xSense improves the baseline model both more significantly and consistently. These results clearly show that the domain-specific knowledge captured by the xSense KB is beneficial to ABSA tasks. Review Comprehension Xu et al.