This paper describes our system to assess humour intensity in edited news headlines as part of a participation in the 7th task of SemEval-2020 on "Humor, Emphasis and Sentiment". Various factors need to be accounted for in order to assess the funniness of an edited headline. We propose an architecture that uses hand-crafted features, knowledge bases and a language model to understand humour, and combines them in a regression model. Our system outperforms two baselines. In general, automatic humour assessment remains a difficult task.
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