A Self-Weighting Module to Improve Sentiment Analysis
Ka‐Hou Chan, Sio‐Kei Im, Yunfeng Zhang · 2021
This article introduces a self-weighting module for filtering meaningless words and normalizing them before RNN encoding, with the purpose of alleviating the long-term dependencies problem. We make use of the concept of weights in our design to analyze the transition of hidden states and indicate the complete architecture for processing the weighted feature and embedded word within the proposed module. In particular, we investigate the conditions that can enhance convergence and show that the proposed classifiers are able to improve the accuracy in the experimental cases significantly, not only giving better performance but also producing faster convergence. Moreover, the proposed module is general and can be applied to all RNN related network models.