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| Automatic Recognition and Bibliometric Analysis of Cited Books |
| Huang Shuiqing1,2, Zhou Hao1,2, Peng Qiuru1,2, Wang Dongbo1,2 |
1.College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095
2.Research Center for Correlation of Domain Knowledge, Nanjing Agricultural University, Nanjing 210095 |
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Abstract There are several citations of ancient books, which are called cited books. Present citation analysis focuses mostly on modern texts; the academic community pays less attention to the citation phenomenon in the texts of ancient books. In this paper, we apply the citation analysis method to ancient books and calculate and analyze the citation indicators of cited books in order to establish a preliminary framework for the bibliometrics research of cited books. This article takes Lunyu Zhushu, Maoshi Zhengyi, and Chunqiu Zuozhuan Zhengyi in Notes of Thirteen Classics as the sample. First, citation items from ancient books are automatically recognized based on CRF (conditional random field), Bi-LSTM (bidirectional long short-term memory) and Bi-LSTM-CRF models and compared their extracted features. Then, the citation analysis method is used to calculate and analyze various citation measurement indexes of these three classic books in order to examine the knowledge correlation between ancient books and discuss the citation behavior of ancient scholars. The results show that the machine learning model applied to the automatic recognition of citation items has a good overall effect, the two deep learning models perform better, and there is an obvious gap between CRF models. Among the two deep learning models, the Bi-LSTM-CRF model is slightly better than the Bi-LSTM. The scale of cited books is affected by various factors, and the cited times of classic documents account for the highest proportion, especially the ritual documents in classic documents. In addition, the ancient people’s citation behavior was influenced by multiple factors such as the purpose of the book, the scholars’ knowledge background, and the difficulty of obtaining the cited documents.
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Received: 08 July 2020
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