@inproceedings{mishra-mishra-2020-scubed-3c,
    title = "Scubed at 3{C} task {B} - A simple baseline for citation context influence classification",
    author = "Mishra, Shubhanshu  and
      Mishra, Sudhanshu",
    editor = "Knoth, Petr  and
      Stahl, Christopher  and
      Gyawali, Bikash  and
      Pride, David  and
      Kunnath, Suchetha N.  and
      Herrmannova, Drahomira",
    booktitle = "Proceedings of the 8th International Workshop on Mining Scientific Publications",
    month = "05 " # aug,
    year = "2020",
    address = "Wuhan, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.wosp-1.10/",
    pages = "65--70",
    abstract = "We present our team Scubed{'}s approach in the 3C Citation Context Classification Task, Subtask B, citation context influence classification. Our approach relies on text based features transformed via tf-idf features followed by training a variety of simple models resulting in a strong baseline. Our best model on the leaderboard is a random forest classifier using only the citation context text. A replication of our analysis finds logistic regression and gradient boosted tree classifier to be the best performing model. Our submission code can be found at: \url{https://github.com/napsternxg/Citation_Context_Classification}."
}