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| Deep Learning-Based Classification of Pre-Qin Classics Questions |
| Wang Dongbo1, Gao Ruiqing1, Shen Si2, Li Bin3 |
1. College of Information Science and Technology, Nanjing Agricultural University, Nanjing, 210095;
2. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094;
3. College of Literature, Nanjing Normal University, Nanjing, 210097 |
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Abstract In recent years, the automated question answering system has become a research hotspot in the fields of machine learning, information retrieval, and natural language processing. This question answering system provides simple and accurate answers in a natural language to the questions posed by users. Since question classification is the first step toward developing a question answering system, the classification results make a direct impact on the quality of a question answering system. However, most of the current question classification research in the field focuses on modern Chinese, and there are relatively few studies on the classification of the questions related to ancient Chinese. This paper starts with the concept of question classification and constructs the question classification system for ancient documents; and then uses TF-IDF to extract the category feature words. We use a support vector machine, conditional random fields, and a deep learning model, to conduct the classics question automatic classification experiment. The results show that the Bi-LSTM model offers the best classification among the three, and delivered a reconciliation average of 94.78 on the seven categories proposed in this paper, which has a strong application value.
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Received: 06 February 2018
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