期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 3, 页码 973-982出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3036192
关键词
Road transportation; Logic gates; Computer architecture; Convolution; Training; Standards; Electronic mail; Convolutional neural network (CNN); dynamic embedding projection gate; multi-class and multi-label text classification; natural language processing (NLP)
类别
资金
- Natural Science Foundation of China [51775385, 61703279]
- Strategy Research Project of Artificial Intelligence Algorithms of Ministry of Education of China
- Fundamental Research Funds for the Central Universities
- Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [RG-48-13540]
The study proposes a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Using the dynamic embedding projection gate (DEPG) allows better capture of word information and control over the integration of context information.
Text classification is a fundamental and important area of natural language processing for assigning a text into at least one predefined tag or category according to its content. Most of the advanced systems are either too simple to get high accuracy or centered on using complex structures to capture the genuinely required category information, which requires long time to converge during their training stage. In order to address such challenging issues, we propose a dynamic embedding projection-gated convolutional neural network (DEP-CNN) for multi-class and multi-label text classification. Its dynamic embedding projection gate (DEPG) transforms and carries word information by using gating units and shortcut connections to control how much context information is incorporated into each specific position of a word-embedding matrix in a text. To our knowledge, we are the first to apply DEPG over a word-embedding matrix. The experimental results on four known benchmark datasets display that DEP-CNN outperforms its recent peers.
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