We describe the system that our FMI@SU students team built for participating in 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 a score, on a scale from 0 to 7, that measures the relevance of the statement expressed by the triple: e.g., how well does the profession of an Actor fit for Quentin Tarantino? We propose a distant supervision approach using information crawled from Wikipedia, DeletionPedia, and DBpedia, together with task-specific word embeddings, TF-IDF weights, and role occurrence order, which we combine in a linear regression model. The official evaluation ranked our submission 1st on Kendalls Tau, 7th on Average score difference, and 9th on Accuracy, out of 21 participating teams.
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