Document Classification using Recurrent Neural Network with Word Sense and Contexts

KIPS Transactions on Software and Data Engineering, Vol. 7, No.7, pp.259-266, July 2018
10.3745/KTSDE.2018.7.7.259, Full Text

Abstract

In this paper, we propose a method to classify a document using a Recurrent Neural Network by extracting features considering word sense and contexts. Word2vec method is adopted to include the order and meaning of the words expressing the word in the document as a vector. Doc2vec is applied for considering the context to extract the feature of the document. RNN classifier, which includes the output of the previous node as the input of the next node, is used as the document classification method. RNN classifier presents good performance for document classification because it is suitable for sequence data among neural network classifiers. We applied GRU (Gated Recurrent Unit) model which solves the vanishing gradient problem of RNN. It also reduces computation speed. We used one Hangul document set and two English document sets for the experiments and GRU based document classifier improves performance by about 3.5% compared to CNN based document classifier.


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Cite this paper

[KIPS Transactions Style]
J. Joo, N. Kim, H. Yang, and H. Park, "Document Classification using Recurrent Neural Network with Word Sense and Contexts," KIPS Transactions on Software and Data Engineering, Vol.7, No.7, pp.259-266, 2018, DOI: 10.3745/KTSDE.2018.7.7.259.

[IEEE Style]
Jong-Min Joo, Nam-Hun Kim, Hyung-Jeong Yang, and Hyuck-Ro Park, "Document Classification using Recurrent Neural Network with Word Sense and Contexts," KIPS Transactions on Software and Data Engineering, vol. 7, no. 7, pp. 259-266, 2018. DOI: 10.3745/KTSDE.2018.7.7.259.

[ACM Style]
Joo, J., Kim, N., Yang, H., and Park, H. 2018. Document Classification using Recurrent Neural Network with Word Sense and Contexts. KIPS Transactions on Software and Data Engineering, 7, 7, (2018), 259-266. DOI: 10.3745/KTSDE.2018.7.7.259.