APJIS Asia Pacific Journal of Information Systems


The Journal for Information Professionals

Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.

ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)

Editor : Seung Hyun Kim

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Current Issue

Date December 2019
Vol. No. Vol. 29 No. 4
DOI https://doi.org/10.14329/apjis.2019.29.4.771
Page 771~788
Title Text Classification Using Parallel Word-level and Character-level Embeddings in Convolutional Neural Networks
Author Geonu Kim, Jungyeon Jang, Juwon Lee, Kitae Kim, Woonyoung Yeo, Jong Woo Kim
Keyword Word-level Embedding, Character-level Embedding, Convolutional Neural Network, Text Classification
Abstract Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naïve Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.

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