Abstract
This paper presents our submission to SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor. There are two subtasks: A. Pairwise Comparison, and B. Semi-Ranking. Our assumption is that the distribution of humorous and non-humorous texts in real life language is naturally imbalanced. Using Naïve Bayes Multinomial with standard text-representation features, we approached Subtask B as a sequence of imbalanced classification problems, and optimized our system per the macro-average recall. Subtask A was then solved via the Semi-Ranking results. On the final test, our system was ranked 10th for Subtask A, and 3rd for Subtask B.- Anthology ID:
- S17-2063
- 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:
- 380–384
- Language:
- URL:
- https://aclanthology.org/S17-2063
- DOI:
- 10.18653/v1/S17-2063
- Cite (ACL):
- Xiwu Han and Gregory Toner. 2017. QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 380–384, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- QUB at SemEval-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in Twitter (Han & Toner, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2063.pdf