Display options
Cite
Tan Z, Chen J, Kang Q, et al. Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification. IEEE Trans Neural Netw Learn Syst. 2021;doi: 10.1109/TNNLS.2020.3036192.
Tan, Z., Chen, J., Kang, Q., Zhou, M., Abusorrah, A., & Sedraoui, K. (2021). Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification. IEEE transactions on neural networks and learning systems, . https://doi.org/10.1109/TNNLS.2020.3036192
Tan, Zhipeng, et al. "Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification." IEEE transactions on neural networks and learning systems vol. (2021). doi: https://doi.org/10.1109/TNNLS.2020.3036192
Tan Z, Chen J, Kang Q, Zhou M, Abusorrah A, Sedraoui K. Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification. IEEE Trans Neural Netw Learn Syst. 2021 Jan 08; doi: 10.1109/TNNLS.2020.3036192. Epub 2021 Jan 08. PMID: 33417564.
Copy Download .nbib Format: NLM
Share it on

IEEE Trans Neural Netw Learn Syst. 2021 Jan 08; doi: 10.1109/TNNLS.2020.3036192. Epub 2021 Jan 08.

Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification.

IEEE transactions on neural networks and learning systems

Zhipeng Tan, Jing Chen, Qi Kang, MengChu Zhou, Abdullah Abusorrah, Khaled Sedraoui

PMID: 33417564 DOI: 10.1109/TNNLS.2020.3036192

Abstract

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.

Publication Types