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Therefore, we adopted a custom frame repository, max- imizing the reuse of the available ones as much as possible, thus serving as a hybrid between FrameNet and Kicktionary. Moreover, we tried to provide a challenging model for the classification task, prioritizing FEs overlap among frames and LU ambiguity (i.e., focusing on very fine-grained semantics with subtle sense differences). We believe this does not only apply to machines, but also to humans: we can view it as a stress test both for the machine learning and the crowdsourcing parts. A total of 6 frames and 15 FEs are modeled with Italian labels as follows:
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