A multi-label text classification method via dynamic semantic representation model and deep neural network

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 50; no. 8; pp. 2339 - 2351
• Main Authors: Wang, Tianshi, Liu, Li, Liu, Naiwen, Zhang, Huaxiang, Zhang, Long, Feng, Shanshan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2020; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Belief networks; Classification; Clustering; Computer Science; Machine learning; Machines; Manufacturing; Mechanical Engineering; Neural networks; Processes; Representations; Semantics; Text categorization; Words (language); Sparse representation; Word embedding; Neural network; Clustering; Text classification
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-020-01680-w

The increment of new words and text categories requires more accurate and robust classification methods. In this paper, we propose a novel multi-label text classification method that combines dynamic semantic representation model and deep neural network (DSRM-DNN). DSRM-DNN first utilizes word embedding model and clustering algorithm to select semantic words. Then the selected words are designated as the elements of DSRM-DNN and quantified by the weighted combination of word attributes. Finally, we construct a text classifier by combining deep belief network and back-propagation neural network. During the classification process, the low-frequency words and new words are re-expressed by the existing semantic words under sparse constraint. We evaluate the performance of DSRM-DNN on RCV1-v2, Reuters-21578, EUR-Lex, and Bookmarks. Experimental results show that our method outperforms the state-of-the-art methods.