@inproceedings{durlich-2018-kluenicorn,
    title = "{KLUE}nicorn at {S}em{E}val-2018 Task 3: A Naive Approach to Irony Detection",
    author = {D{\"u}rlich, Luise},
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/S18-1099/",
    doi = "10.18653/v1/S18-1099",
    pages = "607--612",
    abstract = "This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on {\textquotedblleft}Irony detection in English tweets{\textquotedblright}. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information."
}