Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Table 2: Model comparison results (%). Average accuracy and macro-F1 score over 3 runs with random initialization. The best two results with each dataset are in bold. The results with ♮ are retrieved from the original papers and the results with ♯ are retrieved from dong2014adaptive. The marker † refers p<0.05 by comparing with ASCNN in paired t-test and the marker ‡ refers p<0.05 by comparing with TNet-LF in paired t-test.
['Model', 'Twitter Acc.', 'Twitter F1', 'Lap14 Acc.', 'Lap14 F1', 'Rest14 Acc.', 'Rest14 F1', 'Rest15 Acc.', 'Rest15 F1', 'Rest16 Acc.', 'Rest16 F1']
The results demonstrate the effectiveness of ASGCN-DG and the insufficiency of directly integrating syntax information into attention mechanism as in he2018effective. Meanwhile, ASGCN-DG performs better than ASGCN-DT by a large margin on Twitter, Lap14, Rest15 and Rest16 datasets. And ASGCN-DT’s result is lower than TNet-LF’s on Lap14. A possible reason is that the information from parents nodes is as important as that from children nodes, so treating dependency trees as directed graphs leads to information loss. Additionally, ASGCN-DG outperforms ASCNN on all datasets except Rest14, illustrating ASGCN is better at capturing long-range word dependencies, while to some extent ASCNN shows an impact brought by aspect-specific masking. We suspect Rest14 dataset is not so sensitive to syntactic information. Moreover, the sentences from Twitter dataset are less grammatical, restricting the efficacy. We conjecture this is likely the reason why ASGCN-DG and ASGCN-DT get sub-optimal results on Twitter dataset.