Feature Weight Tuning for Recursive Neural Networks
Table 2: Binary Sentiment Classification with Supervision only at Sentence Level. Word embeddings are initialized from 300 dimensional embeddings borrowed from GLOVE [6].
['Model Recursive', 'Model Standard (GLOVE)', 'Accuracy 0.730']
As discussed earlier, standard neural models underperform the bag of word models. To note, for derivations of standard neural models such as Standard (learned) and MV-RNN with many more parameters to learn, the performance is even worse due to over-fitting. WNN and BENN , although not significantly output bag of words SVM, generates better results, yielding significant improvement over standard neural models and existing revised versions. As we can observe , the model is capable of learning the proper weight of vocabularies, assigning larger weight values to important sentiment indicators (e.g., wonderful, silly and tedious) and suppressing the influence of less important ones. We attribute the better performance of proposed models over standard neural models to such automatic weight-tuning ability.