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http://dx.doi.org/10.6109/jkiice.2018.22.8.1049

Similar Patent Search Service System using Latent Dirichlet Allocation  

Lim, HyunKeun (Department of Computer Engineering, Paichai University)
Kim, Jaeyoon (Department of Computer Engineering, Paichai University)
Jung, Hoekyung (Department of Computer Engineering, Paichai University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.8, 2018 , pp. 1049-1054 More about this Journal
Abstract
Keyword searching used in the past as a method of finding similar patents, and automated classification by machine learning is using in recently. Keyword searching is a method of analyzing data that is formalized through data refinement. While the accuracy for short text is high, long one consisted of several words like as document that is not able to analyze the meaning contained in sentences. In semantic analysis level, the method of automatic classification is used to classify sentences composed of several words by unstructured data analysis. There was an attempt to find similar documents by combining the two methods. However, it have a problem in the algorithm w the methods of analysis are different ways to use simultaneous unstructured data and regular data. In this paper, we study the method of extracting keywords implied in the document and using the LDA(Latent Semantic Analysis) method to classify documents efficiently without human intervention and finding similar patents.
Keywords
Machine Learning; Document Classification; Similar Patent Search; LDA; Keyword Extract;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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