Automatic Classification of Research Papers Focusing on Elemental Technologies and Their Effects

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  • 要素技術とその効果を用いた学術論文の自動分類
  • ヨウソ ギジュツ ト ソノ コウカ オ モチイタ ガクジュツ ロンブン ノ ジドウ ブンルイ

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Abstract

<p>We propose a method for the automatic classification of research papers in terms of the KAKEN classification index using a machine learning method. This classification index was originally devised to classify reports for the KAKEN research fund in Japan, and it is organized as a three-level hierarchy: Area, Discipline, and Research Field. Traditionally, researcher and conference names are used as cue phrases to classify the research paper efficiently. In addition to these cue phrases, we focus on elemental technologies and their effects, as discussed in each research paper. Examining the use of elemental technology terms used in each research paper and their effects is important for characterizing the research field to which a given research paper belongs. Therefore, we use elemental technology terms and their effects as additional cue phrases for machine-learning-based text classification. To investigate the effectiveness of our method, we conducted some experiments using the KAKEN and CiNii articles data. From the experimental results, we obtained average precision scores of 0.853, 0.712, and 0.615 for the Area, Discipline, and Research Field levels in the KAKEN classification index, respectively. These scores are higher than those for the method not using elemental technologies and their effects. From these results, we confirmed the effectiveness of using elemental technology terms and their effects as cue phrases.</p>

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