Open AccessEnhancing Deep Learning-Based Multi-label Text Classification with Capsule Network
Author(s) -
Siyi Yan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1621/1/012037
Subject(s) - computer science , convolutional neural network , artificial intelligence , task (project management) , feature (linguistics) , deep learning , machine learning , artificial neural network , recurrent neural network , pattern recognition (psychology) , linguistics , philosophy , management , economics
Given a piece of text, multi-label text classification (MLTC) is designed to mark the most relevant one label or multiple labels for the text. Most of the existing MLTC models use convolutional neural network (CNN) as feature extractor, but CNN will lose information when dealing with MLTC task. In this paper, we explore the CNN combined with capsule network for MLTC. We use capsule network instead of pool layer in CNN to extract information related to classification results in high-dimensional features. We also explore the way of combining recurrent neural network (RNN) and CNN to model the characteristics of time and space for capsule network to complete classification. In two open MLTC datasets, our model achieves the better results as the baseline system, which shows the effectiveness of the combination of capsule network and CNN for MLTC.