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2009

A Comparative Study on Generalization of Semantic Roles in FrameNet

10 years 9 months ago
A Comparative Study on Generalization of Semantic Roles in FrameNet
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-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to thematic roles of VerbNet. We also propose feature functions that naturally combine and weight these criteria, based on the training data. The experimental result of the role classification shows 19.16% and 7.42% improvements in error reduction rate and macro-averaged F1 score, respectively. We also provide in-depth analyses of the proposed criteria.
Yuichiro Matsubayashi, Naoaki Okazaki, Jun-ichi Ts
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Yuichiro Matsubayashi, Naoaki Okazaki, Jun-ichi Tsujii
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