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| Feature Extraction of Deep Topic Model for Multi-label Text Classification |
| CHEN Wenshi1, LIU Xinhui1, LU Mingyu1 |
| 1.College of Information Science and Technology, Dalian Maritime University, Dalian 116026 |
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Abstract Traditional single-label feature extraction methods cannot effectively solve the problem of multi-label text classification. Aiming at this problem, a dual model of latent dirichlet allocation(LDA) and long short-term memory(LSTM), deep topic feature extraction model(DTFEM), is proposed in this paper. LDA and LSTM are employed as two channels, respectively. LDA is used to model global features of the text, and LSTM is used to model local features of the text. DTFEM can express the global and local features of the text simultaneously and combine supervised learning and unsupervised learning effectively to realize the feature extraction of different levels of text. Experimental results show that DTFEM is superior to other traditional text feature extraction models and obviously improves the indicators of multi-label text classification tasks.
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Received: 15 May 2019
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| Fund:Supported by National Natural Science Foundation of China(No.61073133,61272369) |
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Corresponding Authors:
LU Mingyu, Ph.D., professor. His research interests include data mining, pattern recognition, machine learning and natural language processing.
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| About author:: CHEN Wenshi, Ph.D. candidate, associate professor. His research interests include natural language processing and text classification;LIU Xinhui, master student. Her research interests include natural language processing and text classification. |
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2019, Vol. 32 
