@inproceedings{han-toner-2017-qub,
    title = "{QUB} at {S}em{E}val-2017 Task 6: Cascaded Imbalanced Classification for Humor Analysis in {T}witter",
    author = "Han, Xiwu  and
      Toner, Gregory",
    editor = "Bethard, Steven  and
      Carpuat, Marine  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      Cer, Daniel  and
      Jurgens, David",
    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S17-2063/",
    doi = "10.18653/v1/S17-2063",
    pages = "380--384",
    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 Na{\"i}ve 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.}
}