Aspect Based Sentiment Analysis with Gated Convolutional Networks
Table 5: The accuracy of ATSA subtask on SemEval 2014 Task 4. ‘*’: the results with SVM are retrieved from NRC-Canada Kiritchenko et al. (2014)
['Models', 'Restaurant Test', 'Restaurant Hard Test', 'Laptop Test', 'Laptop Hard Test']
We apply the extended version of GCAE on ATSA task. On this task, the aspect terms are marked in the sentences and usually consist of multiple words. We compare IAN Ma et al. Tang et al. Wang et al. The models other than GCAE is based on LSTM and attention mechanisms. IAN has better performance than TD-LSTM and ATAE-LSTM, because two attention layers guides the representation learning of the context and the entity interactively. RAM also achieves good accuracy by combining multiple attentions with a recurrent neural network, but it needs more training time as shown in the following section. On the hard test dataset, GCAE has 1% higher accuracy than RAM on restaurant data and 1.7% higher on laptop data. GCAE uses the outputs of the small CNN over aspect terms to guide the composition of the sentiment features through the ReLU gate. Because of the gating mechanisms and the convolutional layer over aspect terms, GCAE outperforms other neural models and basic SVM. Again, large scale sentiment lexicons bring significant improvement to SVM.