Mining based on Extraction and Importance Evaluation Using Multi-Measures Methods for Electronic Documents

Wen Xiong; Zi-Hui Ding

doi:10.1051/itmconf/20171205018

All issues

Volume 12 (2017)

ITM Web Conf., 12 (2017) 05018

References

Open Access

Issue		ITM Web Conf. Volume 12, 2017 The 4^th Annual International Conference on Information Technology and Applications (ITA 2017)


Article Number		05018
Number of page(s)		5
Section		Session 5: Information Processing Methods and Techniques
DOI		https://doi.org/10.1051/itmconf/20171205018
Published online		05 September 2017

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[1] V. I. Levenshtein, Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady, 1966, 10:707–710. [Google Scholar]

[2] M. A. Hasan, W. S. Spangler, T. Griffin, and A. Alba, “COA: finding novel patents through text analysis,” in KDD’09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2009, pp. 1175–1184. [CrossRef] [Google Scholar]

[3] X. Jin, et al., “Patent Maintenance Recommendation with Patent Information Network Model.” IEEE, International Conference on Data Mining IEEE, 2011: 280–289. [Google Scholar]

[4] Yu, Wen Der, and J. Y. Hsu. “Content-based text mining technique for retrieval of CAD documents.” Automation in Construction 31.5 (2013):65–74. [CrossRef] [Google Scholar]

[5] Yafooz, Wael M. S., et al., “Dynamic Semantic Textual Document Clustering Using Frequent Terms and Named Entity.” IEEE, International Conference on System Engineering and Technology IEEE, 2013:336–340. [EDP Sciences] [Google Scholar]

[6] Roberta A, Sinoara, et al., “Named entities as privileged information for hierarchical text clustering.” International Database Engineering & Applications Symposium ACM, 2014:57–66. [Google Scholar]

[7] Montalvo, Soto, R. Martínez, and V. Fresno, “NESM: a named entity based proximity measure for multilingual news clustering.” Procesamiento Del Lenguaje Natural 48(2012):81–88. [Google Scholar]

[8] H. P. Zhang, et al., “HHMM-based Chinese lexical analyzer ICTCLAS.” Sighan Workshop on Chinese Language Processing Association for Computational Linguistics, 2003: pages. 758–759. [Google Scholar]

[9] J. Zhu, M. Zhu, Q. Wang, T. Xiao, Niuparser: A Chinese Syntactic and Semantic Parsing Toolkit, in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: System Demonstrations, 2015, pp. 145–150. [Google Scholar]