@techreport{oai:ipsj.ixsq.nii.ac.jp:00077411,
 author = {柴田, 知秀 and 黒橋, 禎夫 and Tomohide, Shibata and Sadao, Kurohashi},
 issue = {2},
 month = {Sep},
 note = {本論文では述語項構造の共起情報と格フレームを用いることにより，大規模コーパスから事態間知識を獲得する手法について述べる．述語項構造の共起情報はアソシエーション分析を用いて効率的に計算し，述語に対する項の必須性の判断を行なう．そして，格フレームを用いて項のアライメントをとる．16 億文からなる Web コーパスを用いて実験を行なったところ，事態ペアの獲得精度が 96%，項のアライメント精度が 79.1%であり，獲得された事態ペアの数は約 2 万となった．, This paper proposes a method for automatically acquiring strongly-related events from a large corpus using predicate-argument co-occurring statistics and caseframe. The co-occurrence measure is calculated using an association rule mining method, and the importance of an argument for each predicate-argument is judged. Then, the argument alignment in the pair of predicate-arguments is performed by using a caseframe. We conducted experiments using aWeb corpus consisting of 1.6G sentences. The accuracy for the extracted event pairs was 96%, and the accuracy of the argument alignment was 79.1%. The number of acquired event pairs was about 20 thousands.},
 title = {述語項構造の共起情報と格フレームを用いた事態間知識の自動獲得},
 year = {2011}
}