Knowledge Discovering using FrameNet, VerbNet and PropBank
نویسندگان
چکیده
In this paper, we present a high accurate system for FrameNet semantic role classification based on the innovative features derived from a combined use of FrameNet, VerbNet and PropBank. The main property of our approach is a unified view of the above three resources which is theoretically supported by the linking theory. Experiments on Support Vector Machines (SVM) show that our system classifies semantic information with high accuracy, enabling future work for the design of knowledge discovering FrameNet-based models.
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