Natural Language Processing Service Based on Stroke-Level Convolutional Networks for Chinese Text Classification

Abstract

With the development of deep learning and artificial intelligence, more and more research apply neural networks to natural language processing tasks. However, while the majority of these research take English corpus as the dataset, few studies have been done using Chinese corpus. Meanwhile, Existing Chinese processing algorithms typically regard Chinese word or Chinese character as the basic unit but ignore the deeper information into the Chinese character. In Chinese linguistic, strokes are the basic unit of Chinese character who are similar to letters of the English word. Inspired by the recent success of deep learning at character-level, we delve deeper to Chinese stroke level for Chinese language processing and developed it into service for Chinese text classification. In this paper, we dig the basic feature of the strokes considering the similar Chinese character components and propose a new method to leverage Chinese stroke for learning the continuous representation of Chinese character and develop it into a service for Chinese text classification. We develop a dedicated neural architecture based on the convolutional neural network to effectively learn character embedding and apply it to Chinese word similarity judgment and Chinese text classification. Both experiments results show that the stroke level method is effective for Chinese language processing.