HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition

• DOI: 10.18653/v1/s17-2010
• Publication Date: 2018-01-30T09:09:28Z
• Authors: David Donahue; Alexey Romanov; Anna Rumshisky
• Keywords: SemEval; Feature (linguistics); Component (thermodynamics); Representation

This paper describes the winning system for SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. Humor detection has up until now been predominantly addressed using feature-based approaches. Our utilizes recurrent deep learning methods with dense embeddings to predict humorous tweets from @midnight show #HashtagWars. In order include both meaning and sound in analysis, GloVe are combined novel phonetic representation serve as input an LSTM component. The output is character-based CNN model, XGBoost component ensemble model which achieves 0.675 accuracy on evaluation data.