Tree Kernel Engineering for Proposition Re-ranking
نویسندگان
چکیده
Recent work on the design of automatic systems for semantic role labeling has shown that such task is complex from both modeling and implementation point of views. Tree kernels alleviate such complexity as kernel functions generate features automatically and require less software development for data pre-processing. In this paper, we study several tree kernel approaches for boundary detection, argument classification and, most notably, proposition re-ranking. The comparative experiments on Support Vector Machines with such kernels on the CoNLL 2005 dataset show that very simple tree manipulations trigger automatic feature engineering that highly improves accuracy and efficiency in every SRL phase.
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Tree Kernels for Semantic Role Labeling
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