Abstract
With the coming of digital newspaper, user-oriented special topic generation becomes extremely urgent to satisfy the users’ requirements both functionally and emotionally. We propose an applicable automatic special topic generation system for digital newspapers based on users’ interests. Firstly, extract subject heading vector of the topic of interest by filtering out function words, localizing Latent Dirichlet Allocation (LDA) and training the LDA model. Secondly, remove semantically repetitive vector component by constructing a synonymy word map. Lastly, organize and refine the special topic according to the similarity between the candidate news and the topic, and the density of topic-related terms. The experimental results show that the system has both simple operation and high accuracy, and it is stable enough to be applied for user-oriented special topic generation in practical applications.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Fan, J.-R.: Research on Topic Generation and Retrieval of News Video Based on Text. Institute of Computing Technology, Chinese Academy of Science, Beijing (2008)
Li, H.-X., Zhang, H.-P.: Internet hot topic detection based on topic words. In: Proceedings of the 5th China Information Retrieval Conference, Shanghai (2009)
Wang, Z.-M.: Research on Web News Topic Organization and Acquisition System. College of Information Science & Engineering, Central South University (2008)
Cui, J.-M., Liu, J.-M., Liao, Z.-Y.: A Research of Text Categorization Based on Support Vector Machine. Computer Simulation 30(2), 294–299 (2013)
Tan, H., Jia, Z.-Y., Shi, Z.-Z.: How to Organize and Generate News Topics with Great Efficiency. Science & Technology Review 7, 48–51 (2004)
Erk, K., Padó, S.: A structured vector space model for word meaning in context. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2008)
Biggers, L.R., Bocovich, C., Capshaw, R., Eddy, B.P., Etzkorn, L.H., Kraft, N.A.: Configuring Latent Dirichlet Allocation Based Feature Location. Empirical Software Engineering 19(3), 465–500 (2014)
He, D.: Retrospect of and Prospect for Chinese Thesaurus. Information Studies Theory & Application (2010)
Feng, G.-H., Zhen, Z.: Review of Chinese Automatic Word Segmentation. Library and Information Service 55(2), 41–45 (2011)
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Pr., pp. 1–17 (2011)
David, M.B.: Probabilistic Topic Models. Communications of the ACM 55(4), 77–84 (2012)
David, M.B., Andrew, Y.N., Michael, I.J.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Mei, J.-J., Zhu, Y.-M., Gao, Y.-Q.: Cilin-thesaurus of Chinese words. Shanghai Lexicographic Publishing House, Shanghai (1983)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, X., Ye, M., Tang, Z., Xu, JB., Gao, LC. (2015). A User-Oriented Special Topic Generation System for Digital Newspaper. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-25207-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25206-3
Online ISBN: 978-3-319-25207-0
eBook Packages: Computer ScienceComputer Science (R0)