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

David Donahue, Alexey Romanov, Anna Rumshisky


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
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
98–102
Language:
URL:
https://www.aclweb.org/anthology/S17-2010
DOI:
10.18653/v1/S17-2010
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2010.pdf