入力文の格助詞ごとに学習データを分割した機械学習による受身文の能動文への変換における格助詞の変換 Machine-Learning-Based Transformation of Japanese Passive Sentences into Active by Separating Training Data into Each Input Particle

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著者

    • 村田 真樹 MURATA Masaki
    • 独立行政法人情報通信研究機構 知識創成コミュニケーション研究センター Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology
    • 金丸 敏幸 KANAMARU Toshiyuki
    • 独立行政法人情報通信研究機構 知識創成コミュニケーション研究センター Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology
    • 白土 保 [他] SHIRADO Tamotsu
    • 独立行政法人情報通信研究機構 知識創成コミュニケーション研究センター Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology
    • 井佐原 均 ISAHARA Hitoshi
    • 独立行政法人情報通信研究機構 知識創成コミュニケーション研究センター Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology

抄録

We developed a new method of transforming Japanese case particles when transforming Japanese passive sentences into active sentences. This method separates training data into each input particle and uses machine learning for each particle. We also used numerous rich features for learning. Murata et al. conducted a previous study on transforming Japanese passive sentences into active sentences [2]. They used machine learning but did not separate training data for any input particles and did not have many rich features for learning. They achieved an accuracy rate of 89.77%. We added many rich features to those used in Murata et al.'s study and obtained an accuracy rate of 92.00%. In addition, we used our method of separating training data into each input particle and using machine learning for each particle, and obtained an accuracy rate of 94.30%. We confirmed the significance of these improvements through a statistical test. We also conducted experiments utilizing traditional methods using verb dictionaries and manually prepared heuristic rules and confirmed that our method achieved much higher accuracy rates than traditional methods.

収録刊行物

  • システム制御情報学会論文誌

    システム制御情報学会論文誌 21(6), 165-175, 2008-06-15

    一般社団法人 システム制御情報学会

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各種コード

  • NII論文ID(NAID)
    10021064553
  • NII書誌ID(NCID)
    AN1013280X
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13425668
  • NDL 記事登録ID
    9526248
  • NDL 雑誌分類
    ZM11(科学技術--科学技術一般--制御工学)
  • NDL 請求記号
    Z14-195
  • データ提供元
    CJP書誌  NDL  J-STAGE 
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