@inproceedings{liu-2018-emonlp,
    title = "{E}mo{NLP} at {S}em{E}val-2018 Task 2: {E}nglish Emoji Prediction with Gradient Boosting Regression Tree Method and Bidirectional {LSTM}",
    author = "Liu, Man",
    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/S18-1059/",
    doi = "10.18653/v1/S18-1059",
    pages = "390--394",
    abstract = "This paper describes our system used in the English Emoji Prediction Task 2 at the SemEval-2018. Our system is based on two supervised machine learning algorithms: Gradient Boosting Regression Tree Method (GBM) and Bidirectional Long Short-term Memory Network (BLSTM). Besides the common features, we extract various lexicon and syntactic features from external resources. After comparing the results of two algorithms, GBM is chosen for the final evaluation."
}