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A Multi-label Classification Bot for Issue Management System in GitHub
Doje Park, Yyejin Yang, Gwang Choi, Seonah Lee, Sungwon Kang
http://doi.org/10.5626/JOK.2021.48.8.928
The GitHub platform, on which many developers develop open-source software projects, provides an issue management system. Using the system, the stakeholders can report software problems or functional requests as issues. The issue management system provides issue report forms and allows developers to create and use labels to classify issues. However, since the labeling work is manually done, it requires a lot of effort from the developers and inaccurate labeling can easily occur. In addition, it takes a lot of time for a project manager to read and give feedback on each issue. To mitigate this problem, previous studies have proposed attaching a single label to an issue automatically. However, in practice, there are a number of issue reports that need multiple labels to be attached. Therefore, in this study, we proposed a multi-labeling bot that automatically attaches multiple labels to an issue report in order to reduce the effort required by a project manager to read issue reports and give feedback in GitHub. The multi-label classification of our bot showed F-score ranging from 0.54 to 0.78.
Comparative Analysis of Various Korean Morpheme Embedding Models using Massive Textual Resources
http://doi.org/10.5626/JOK.2019.46.5.413
Word embedding is a transformation technique that enables a computer to recognize natural language. It is used in various fields of natural language processing based on machine learning such as machine translation and named-entity recognition. Various word-embedding models are available; however, few studies have compared the performance of these models under similar conditions. In this paper, we compare and analyze the performance of Word2Vec Skip-Gram, CBOW, Glove, and FastText, which are actively used according to Korean morpheme spacing. Based on experimental results with large news corpus and Sejong corpus, FastText yielded the best performance among CBOW, Skip-gram, Glove, and FastText of Word2Vec.
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