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

Tianshi Wang, Li Liu, Naiwen Liu, Huaxiang Zhang, Long Zhang, Shanshan Feng

Abstract: 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.