[Updated on Apr. 18] Integration of CiNii Articles into CiNii Research


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  • アブストラクト オ モチイタ ゲンシ ブンシ ブツリガク ブンヤ ノ ロンブン ブンルイ シエン システム ノ セッケイ ト ジッソウ
  • Design and Implementation of an Atomic and Molecular Physics Paper Classification Supporting System Using Abstracts

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The cross section data of ionization and excitation by collision among atoms, mulecules and electrons is very useful in various research fields. We can obtain such data out of online jounals of atomic and molecular physics research as digital papers. To obtain the data, we need to classify online papers. However, the classification of the online papers is difficult because there are just abstracts available for free on web pages in general. In this paper, we design a paper classification supporting system to find papers including the data, using just abstracts. We apply a machine learning technique, which is a conventional text classification method, to the system in order to prove that our system using just abstracts is efficient.


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