Abstract
Dream of the Red Chamber (DRC), written in Qing dynasty, is a prestigious classical novel in Chinese literature. There exists a disputation over the authorship of DRC for the last 40 chapters. This research makes an effort to explore the DRC’s authorship from the perspective of link prediction. At first, segmentation, part-of-speech tagging and named entity recognition are performed on the Chinese text of the DRC novel. A social network representing the relationship of characters is constructed based on the co-word analysis. Link is weighted according to the co-occurrence of two characters in a sentence or a paragraph. Furthermore, link prediction is completed on two groups of datasets: first 80 chapters and the whole 120 chapters. Two link prediction approaches are utilized in this research, including the similarity-based method and the classification-based method. Finally, the experiments lead to a conclusion that the author of the last 40 chapters is different from the first 80 chapters with a high probability.
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References
Wei, B.: Statistical analysis on the differences of writing style between first 80 chapters and last 40 chapters in “dream of red mansions”: an application of equivalent test on two independent binominal populations. Chinese J. Appl. Probab. Statist. 25(4), 441–448 (2009)
Shi, J.: The authorship research on a dream of red mansions based on support vector machine. Stud. “A Dream Red Mansions” (5), 35–52 (2011)
Li, H., Liu, Y.: Language models and classification analysis for dream of the red chamber. In: Proceedings of the 2nd Conference on Cloud Computing & Intelligence Systems, Hangzhou, China, pp. 1459–1464 (2012)
Xiao, T., Liu, Y.: Words and n-gram models analysis for “A Dream of Red Mansions.” New Technol. Libr. Inf. Serv. 4, 50–57 (2015)
Ye, L.: The authorship research on A Dream of Red Mansions based on clustering of statistical stylistic features. Stud. “A Dream of Red Mansions” 5, 312–324 (2016)
Jiang, N.: A study of the author of a dream of red mansions based on machine learning. Master thesis, Zhejiang University, Hangzhou (2018)
Hou, X., Liu, Y., Li, Z.: Convolutional adaptive network for link prediction in knowledge bases. Appl. Sci. 11(9), 4270 (2021)
Wang, G., Wang, Y., Li, J., Liu, K.: A multidimensional network link prediction algorithm and its application for predicting social relationships. J. Comput. Sci. 53, 101358 (2021)
Ajay, K., et al.: Link prediction techniques, applications, and performance: a survey. Physica A 553, 124289 (2020)
Cao, X., Gao, E.: A Dream of Red Mansions. People’s Literature Publishing House, Beijing (2000)
Fan, C.: Research on relationships of characters in the dream of the red chamber based on co-word analysis. ICIC Express Lett. Part B Appl. 11(5), 1–8 (2020)
Martínez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. 49(4), 69.1–69.33 (2016)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Acknowledgement
This work was supported by the High-level Innovation and Entrepreneurship Talents Introduction Program of Jiangsu Province of China, 2019.
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Fan, C., Li, Y. (2021). Research on the Authorship of Dream of the Red Chamber Based on Link Prediction. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_38
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DOI: https://doi.org/10.1007/978-3-030-84529-2_38
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