Research and Implementation of Text  Similarity System Based on Power  Spectrum Analysis

Ying Xie; Shouning Qu; Huanhuan Song

doi:10.4236/jcc.2014.26002

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JCC> Vol.2 No.6, April 2014

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Research and Implementation of Text Similarity System Based on Power Spectrum Analysis

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DOI: 10.4236/jcc.2014.26002 3,224 Downloads 5,041 Views Citations

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Ying Xie, Shouning Qu, Huanhuan Song

Affiliation(s)

Information Network Center, University of Jinan, Jinan, China.
School of Information Science and Engineering, University of Jinan, Jinan, China.

ABSTRACT

The paper proposed the research and implement of text similarity system based on power spectrum analysis. It is not difficult to imagine that the signals of brain are closely linked with writing process. So we build text modeling and set pulse signal function to get the power spectrum of the text. The specific detail is getting power spectrum from economic field to build spectral library, and then using the method of power spectrum matching algorithm to judge whether the test text belonged to the economic field. The method made text similarity system finish the function of text intelligent classification efficiently and accurately.

KEYWORDS

Pulsing Signal Function, Power Spectrum Matching Algorithm, Text Similarity System, Text Intelligent Classification

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Xie, Y. , Qu, S. and Song, H. (2014) Research and Implementation of Text Similarity System Based on Power Spectrum Analysis. Journal of Computer and Communications, 2, 7-17. doi: 10.4236/jcc.2014.26002.

References

[1]	Lambros, C., Harris, P. and Stelios, P. (1994) A Matching Technique in Example-Based Machin Translation. In: Proceedings of COLING’94, Association for Computational Linguistics, Stroudsburg, 100-104.

[2]	Taylor, W. and Wang, J.Z. (2007) Concept Forest: A New Ontology-Assisted Text Document Similarity Measurement Method. In: 2007 ACM International Conference on Web Intelligence, Fremont, 2-5 November 2007, 395-401.

[3]	Xiong, C.G. and Tian, H. (2010) Improved Text Similarity Model Based on Page Rank Value. Network Security Technology and Application, 30, 23-25.

[4]	Wu, K. and Zhou, X.Z. (2010) Concept Semantic Similarity Algorithm Based on Bayesian Estimation. Journal of Chinese Information, 24, 52-57.

[5]	Charniak, E. (2011) The Brain as a Statistical Inference Engine—And You Can Too. Computational Linguistics, 37, 643-655.

[6]	Zhang, M.M., Qu, S.N. and Du, T. (2013) Subject Thesaurus Automatic Construction Based on Multidomain Distribution Entropy. Journal of Computational Information Systems, 9, 3485-3492.

[7]	Turney, P.D. and Pantel, P. (2010) From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research, 37, 141-188.

[8]	Zhou, J.G. and Luo, X.S. (2008) The Study and Analysis of EEG Features. Master’s Thesis, Guangxi Normal University, Guilin.

[9]	Chen, H.J. (2004) Digital Signal Processing. Higher Education Press, Beijing, 266-299.

[10]	The Center Research of Feisi Science and Technology (2005) The Auxiliary Signal Processing Technology and Application by MATLAB. Electronic Industry Press, Beijing, 293-328.

[11]	Alon, E. and Fan, Q.F. (2007) Curve Matching, Time Warping, and Light Fields: New Algorithms for Computing Similarity between Curves. Journal of Mathematical Imaging and Vision, 27, 203-216.

[12]	Zhu, J. (2008) A New Curve Research on Similarity Criterion. Master’s Thesis, Wuhan University of Technology, Wuhan.