An enhanced Tree-LSTM architecture for sentence semantic modeling using typed dependencies
Table 6: Comparison of Typed Dependency Tree-LSTM with LSTM models for binary sentiment classification on SST dataset. The values are taken from previously published results.
['Model', 'Accuracy(%)']
We found that the proposed model outperformed standard LSTM as well as DT-LSTM in the accuracy of prediction. The accuracy is comparable with that of bidirectional LSTM but lesser than that of the constituency tree-LSTM (CT-LSTM). The results clearly indicate that Typed LSTM is superior to DT-LSTM in recognizing sentiments of text.