Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
StateAccepted/In press - 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Computer architecture
  • Convolution
  • Convolutional neural network (CNN)
  • Electronic mail
  • Logic gates
  • Road transportation
  • Standards
  • Training
  • dynamic embedding projection gate
  • multi-class and multi-label text classification
  • natural language processing (NLP).

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