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We describe the system that our FMI@SU student's team built for participatingin the Triple Scoring task at the WSDM Cup 2017. Given a triple from a"type-like" relation, profession or nationality, the goal is to produce ascore, on a scale from 0 to 7, that measures the relevance of the statementexpressed by the triple: e.g., how well does the profession of an Actor fit forQuentin Tarantino? We propose a distant supervision approach using informationcrawled from Wikipedia, DeletionPedia, and DBpedia, together with task-specificword embeddings, TF-IDF weights, and role occurrence order, which we combine ina linear regression model. The official evaluation ranked our submission 1st onKendall's Tau, 7th on Average score difference, and 9th on Accuracy, out of 21participating teams.

Finding People's Professions and Nationalities Using Distant Supervision - The FMI@SU "goosefoot" team at the WSDM Cup 2017 Triple Scoring Task