Abstract
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 system utilizes recurrent deep learning methods with dense embeddings to predict humorous tweets from the @midnight show #HashtagWars. In order to include both meaning and sound in the analysis, GloVe embeddings are combined with a novel phonetic representation to serve as input to an LSTM component. The output is combined with a character-based CNN model, and an XGBoost component in an ensemble model which achieves 0.675 accuracy on the evaluation data.- Anthology ID:
- S17-2010
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 98–102
- Language:
- URL:
- https://aclanthology.org/S17-2010
- DOI:
- 10.18653/v1/S17-2010
- Cite (ACL):
- David Donahue, Alexey Romanov, and Anna Rumshisky. 2017. HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 98–102, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition (Donahue et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2010.pdf