Journal Article

Short Text Classification Based on Explicit and Implicit Multiscale Weighted Semantic Information

Ma, Zhilong, Zhang, Juling, Lv, Xiaoyi, Gong, Jun, Guo, Wenqiang

2023

Overview

Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial neural networks / Classification / Computational linguistics / Data mining / Deep learning / Language processing / Methods / Mineral industry / Mining / Mining industry / Natural language interfaces / Neural networks / Semantics / Text categorization / Weighting