@inproceedings{liu-etal-2020-scmhl5,
    title = "Scmhl5 at {TRAC}-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach",
    author = "Liu, Han  and
      Burnap, Pete  and
      Alorainy, Wafa  and
      Williams, Matthew",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Lahiri, Bornini  and
      Zampieri, Marcos  and
      Malmasi, Shervin  and
      Murdock, Vanessa  and
      Kadar, Daniel",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.trac-1.10/",
    pages = "62--68",
    language = "eng",
    ISBN = "979-10-95546-56-6",
    abstract = "This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification ({\textquoteleft}Overtly Aggressive', {\textquoteleft}Covertly Aggressive' and {\textquoteleft}Non-aggressive') and English Sub-task B on binary classification for Misogynistic Aggression ({\textquoteleft}gendered' or {\textquoteleft}non-gendered'). For both sub-tasks, our method involves using the pre-trained Bert model for extracting the text of each instance into a 768-dimensional vector of embeddings, and then training an ensemble of classifiers on the embedding features. Our method obtained accuracy of 0.703 and weighted F-measure of 0.664 for Sub-task A, whereas for Sub-task B the accuracy was 0.869 and weighted F-measure was 0.851. In terms of the rankings, the weighted F-measure obtained using our method for Sub-task A is ranked in the 10th out of 16 teams, whereas for Sub-task B the weighted F-measure is ranked in the 8th out of 15 teams."
}