Impact of convolutional neural network and FastText embedding on text classification

• Published in: Multimedia tools and applications Vol. 82; no. 4; pp. 5569 - 5585
• Main Authors: Umer, Muhammad, Imtiaz, Zainab, Ahmad, Muhammad, Nappi, Michele, Medaglia, Carlo, Choi, Gyu Sang, Mehmood, Arif
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2023; Springer Nature B.V
• Subjects: 1178: Pattern Recognition for Adaptive User Interfaces; Artificial intelligence; Artificial neural networks; Automatic classification; Benchmarks; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Deep learning; Embedding; Experimentation; Experiments; Image analysis; Machine learning; Multimedia; Multimedia Information Systems; Natural language; Neural networks; Object recognition; Pattern analysis; Pattern recognition; Regression analysis; Representations; Special Purpose and Application-Based Systems; Text categorization; Text mining; Deep learning; Natural language processing; Convolutional Neural Network (CNN); FastText

Efficient word representation techniques (word embeddings) with modern machine learning models have shown reasonable improvement on automatic text classification tasks. However, the effectiveness of such techniques has not been evaluated yet in terms of insufficient word vector representation for tr...