A Comparative Study on Generalization of Semantic Roles in FrameNet

  title={A Comparative Study on Generalization of Semantic Roles in FrameNet},
  author={Yuichiroh Matsubayashi and Naoaki Okazaki and Jun'ichi Tsujii},
A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. These corpora define the semantic roles of predicates for each frame independently. Thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances. This paper explores several criteria for generalizing semantic roles in FrameNet: role hierarchy, human… CONTINUE READING
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Key Quantitative Results

  • The experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1 score, respectively.


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