抄録
行為-効果,行為-目的のような事態間の関係を大規模コーパスから自動的に獲得する.文内共起パターンを利用する手法では,事態間でどの項が共有されるかの知識を獲得することが難しい.そこで事態間で共有される名詞(アンカー)を用いて項共有情報を獲得し,文内共起パターンによる事態間関係と組み合わせることで項を必要とする事態間関係を獲得する.このとき2種類の異なるアンカーを用いることで,精度を保ったまま再現率を向上できることを確認した.
Addressing the task of acquiring semantic relations between events from a large corpus, we first argue the complementarity between the pattern-based relation-oriented approach and the anchor-based argument-oriented approach. We then proposes a two-phased approach, which first uses lexico-syntactic patterns to acquire predicate pairs and then uses two types of anchors to identify shared arguments. The present results of our empirical evaluation on a large-scale Japanese Web corpus have shown that (a) the anchor-based filtering extensively improves the precision of predicate pair acquisition, (b) the two types anchors are almost equally contributive and combining them improves recall without losing precision, and (c) the anchor-based method achieves high precision also in shared argument identification.
