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Abstract

In response to the CLEF eRisk 2019 shared task on measuring the severity of the signs of depression from threads of user submissions on social media, our team has developed a data-driven, ensemble model approach. Our system leverages word polarities, token extraction via mutual information, keyword expansion and semantic similarities for classifying Reddit posts according to the Beck’s Depression Inventory (BDI). Individual models were combined at the post level by majority voting. The approach achieved a baseline performance for the assessed metrics, including Average Hit Rate and Depression Category Hit Rate, being equivalent to the median system in the limit of one standard deviation.

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