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Journal of the Korean Society for Library and Information Science

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Vol.53 No.2
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A Study on Patent Literature Classification Using Distributed Representation of Technical Terms

Journal of the Korean Society for Library and Information Science / Journal of the Korean Society for Library and Information Science, (P)1225-598X; (E)2982-6292
2019, v.53 no.2, pp.179-199
https://doi.org/10.4275/KSLIS.2019.53.2.179


& (2019). A Study on Patent Literature Classification Using Distributed Representation of Technical Terms. Journal of the Korean Society for Library and Information Science, 53(2), 179-199, https://doi.org/10.4275/KSLIS.2019.53.2.179
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Abstract

In this paper, we propose optimal methodologies for classifying patent literature by examining various feature extraction methods, machine learning and deep learning models, and provide optimal performance through experiments. We compared the traditional BoW method and a distributed representation method (word embedding vector) as a feature extraction, and compared the morphological analysis and multi gram as the method of constructing the document collection. In addition, classification performance was verified using traditional machine learning model and deep learning model. Experimental results show that the best performance is achieved when we apply the deep learning model with distributed representation and morphological analysis based feature extraction. In Section, Class and Subclass classification experiments, We improved the performance by 5.71%, 18.84% and 21.53%, respectively, compared with traditional classification methods.

keywords
Patent Literature Classification, Distributed Representation, Word Embedding Vector, Deep Learning, 특허문헌 분류, 분산표현, 워드 임베딩 벡터, 딥러닝

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