A Deeping Learning-based Article- and Paragraph-level Classification

Kim， Euhee

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2018, 23(11), pp.31-41
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Kim， Euhee ¹

¹신한대학교

ABSTRACT

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.
KEYWORDS

Genre Classification, Deep Learning, Word2Vec, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Networks (CNN), Word embedding
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