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Conference Paper CNN based Sentence Classification with Semantic Features using Word Clustering
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Authors
Hwa-Yeon Kim, Jinsu Lee, Na Young Yeo, Marcella Astrid, Seung-Ik Lee, Young-Kil Kim
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.484-488
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539546
Abstract
Text classification is one of the natural language processing (NLP) methods that assigns texts to one or more categories. In this paper, we propose a text classification method based on deep neural networks and word clustering. We also provide analysis on the effects of the number of channels, the way of converting the cluster information to a vector, and the update method of the input channel during learning with baseline. To show the effectiveness of our approach, we apply the method to the TREC question dataset and Movie Review dataset. From the results, we confirm that semantic features from word clustering is able to increase the classification accuracy by 1.96%.
KSP Keywords
Classification method, Cluster information, Deep neural network(DNN), Movie Review, Natural Language Processing, Sentence Classification, Word clustering, classification accuracy, semantic features, text classification