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.

新单词和文本类别的增加需要更准确和可靠的分类方法。在本文中，我们提出了一种结合动态语义表示模型和深度神经网络（DSRM-DNN）的新颖的多标签文本分类方法。DSRM-DNN首先利用词嵌入模型和聚类算法选择语义词。然后，将所选单词指定为DSRM-DNN的元素，并通过单词属性的加权组合进行量化。最后，我们通过结合深度信念网络和反向传播神经网络来构造文本分类器。在分类过程中，低频词和新词在稀疏约束下被现有的语义词重新表达。我们评估DSRM-DNN在RCV1-v2，Reuters-21578，EUR-Lex和书签上的性能。