ProTestA: Identifying and Extracting Protest Events in News Notebook for ProtestNews Lab at CLEF 2019

By:Angelo Basile, Tommaso Caselli

Year: 2019


This notebook describes our participation to the ProtestNew Lab, identifying protest events in news articles in English. Systems
are challenged to perform unsupervised domain adaptation against three sub-tasks: document classification, sentence classification, and event extraction. We describe the final submitted systems for all sub-tasks, as well as a series of negative results. Results indicate pretty robust performances in all tasks (average F1 of 0.705 for the document classification sub-task, average F1 of 0.592 for the sentence classification sub-task; average F1 0.528 for the event extraction sub-task), ranking in the top 4 systems, although drops in the out-of-domain test sets are not minimal.

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