Comparison of word embeddings in text classification based on RNN and CNN
Merlin Susan David, Shini Renjith
Abstract
Abstract This paper presents a comparison of word embeddings in text classification using RNN and CNN. In the field of image classification, deep learning methods like as RNN and CNN have shown to be popular. CNN is most popular model among deep learning techniques in the field of NLP because of its simplicity and parallelism, even if the dataset is huge. Word embedding techniques employed are GloVe and fastText. Use of different word embeddings showed a major difference in the accuracy of the models. When it comes to embedding of rare words, GloVe can sometime perform poorly. Inorder to tackle this issue, fastText method is used. Deep neural networks with fastText showed a remarkable improvement in the accuracy than GloVe. But fastText took some time to train when compared to GloVe. Further, the accuracy was improved by minimizing the batch size. Finally we concluded that the word embeddings have a huge impact on the performance of text classification models