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Automatic Classification of Academic Articles Using BERT Model Based on Deep Learning

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2022, v.39 no.3, pp.293-310
https://doi.org/10.3743/KOSIM.2022.39.3.293
In hu Kim
Seong hee Kim
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Abstract

In this study, we analyzed the performance of the BERT-based document classification model by automatically classifying documents in the field of library and information science based on the KoBERT. For this purpose, abstract data of 5,357 papers in 7 journals in the field of library and information science were analyzed and evaluated for any difference in the performance of automatic classification according to the size of the learned data. As performance evaluation scales, precision, recall, and F scale were used. As a result of the evaluation, subject areas with large amounts of data and high quality showed a high level of performance with an F scale of 90% or more. On the other hand, if the data quality was low, the similarity with other subject areas was high, and there were few features that were clearly distinguished thematically, a meaningful high-level performance evaluation could not be derived. This study is expected to be used as basic data to suggest the possibility of using a pre-trained learning model to automatically classify the academic documents.

keywords
automatic classification, deep learning, BERT model, transformer, natural language processing
Submission Date
2022-08-21
Revised Date
2022-09-07
Accepted Date
2022-09-13

Journal of the Korean Society for Information Management