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Research on Concept-based Chinese Literature Retrieval Methods
DOI: https://doi.org/10.62381/I245703
Author(s)
Peng Xing
Affiliation(s)
Library & Information Management Center, Zhejiang Police Vocational Academy, Hangzhou, Zhejiang, China
Abstract
This study briefly reviews the development of computer-based literature retrieval and summarizes the main issues involved in the research process of Chinese literature retrieval. In response to relevant problems in Chinese literature retrieval, a retrieval model based on "concept groups" was constructed. The model was preliminarily implemented and tested in the study. The test results show that the retrieval model can improve the efficiency of Chinese literature retrieval within a limited scope, reduce the learning and comprehension costs for users, and enhance the user experience and retrieval efficiency in the field of Chinese literature retrieval, providing a concrete direction for its development.
Keywords
Concept Groups; Literature Retrieval; Chinese Literature; Data Processing
References
[1]Snyder, S. Samuel. Computer Advances Pioneered by Cryptologic Organizations. Annals of the History of Computing, 1980, 2(1): 60-70. [2]J. M. Griffiths, D. W. King. US information retrieval system evolution and evaluation (1945-1975). IEEE Annals of the History of Computing, 2002, 24(3): 35-55. [3]C. Z. Wang. The development of China's scholarly publications in library and information science, 1979-2009: An analysis of ISI literature. Library Management, 2011, 32: 435-443. [4]C. Cao, Q. Feng, Y. Gao, et al. Progress in the development of national knowledge infrastructure. Journal of Computer Science and Technology, 2002, 17: 523-534. [5]X. P. Qiu, Z. Qi, X. J. Huang. FudanNLP: A Toolkit for Chinese Natural Language Processing. ACL 2013, 2013: 49. [6]X. Liu, S. Wang, S. Lu, et al. Adapting feature selection algorithms for the classification of Chinese texts. Systems, 2023, 11(9): 483. [7]J. He, E. Meij, M. Rijke. Result diversification based on query‐specific cluster ranking. Journal of the American Society for Information Science and Technology, 2011, 62(3): 550-571. [8]A. K. Nikhath, K. Subrahmanyam. Feature selection, optimization and clustering strategies of text documents. International Journal of Electrical and Computer Engineering (IJECE), 2019, 9(2): 1313-1320. [9]R. J. Whittaker, F. Rigal, P. A. V. Borges, et al. Functional biogeography of oceanic islands and the scaling of functional diversity in the Azores. Proceedings of the National Academy of Sciences, 2014, 111(38): 13709-13714. [10]Y. Zhang. A Study of Automated Deep Classification of Literature Based on Chinese Library Classification. Libraly Journal, 2024, 43(395): 61.
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