2014 年 9 巻 4 号 p. 736-770
Numerous studies have applied machine-learning approaches to semantic role labeling with the availability of corpora such as FrameNet and PropBank. These corpora define frame-specific semantic roles for each frame, which are problematic for a machine-learning approach because the corpus contains a number of infrequent roles that hinder efficient learning. This paper focuses on the generalization problem of semantic roles in a semantic role labeling task. We compare existing generalization criteria with our novel criteria, and clarify the characteristics of each criterion. We also show that using multiple generalization criteria in a single model improves the performance of a semantic role classification. In experiments on FrameNet, we achieved 19.16% error reduction in terms of total accuracy, and 7.42% in macro-averaged F1. On PropBank, we reduced 24.07% of errors in total accuracy, and 26.39% of errors in the evaluation for unseen verbs.