n-BiLSTM: BiLSTM with n-gram Features for Text Classification
Yunxiang Zhang, Zhuyi Rao · 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) · 2020
Text classification is widely existing in the fields of e-commerce and log message analysis. Besides, it is an essential module in text processing tasks. In this paper, we present a method to create an accurate and fast text classification system in both One-vs.-one and One-vs.-rest manner. Our approach, named n-BiLSTM, is used to convert natural text sentences into features similar to bag-of-words with n-gram techniques, and then the features are fed into a bidirectional LSTM. The two components are able to take better advantages of multi-scale feature representation and context information. Finally, the whole system is evaluated using two labeled movie review datasets, IMDB and SSTb, to test one-vs.-one and one-vs.-rest performances respectively. The results obtained show that our n-BiLSTM algorithm is superior to the basic LSTM and bidirectional LSTM algorithms.