@inproceedings{mohammadi-etal-2019-clac,
    title = "{CL}a{C} Lab at {S}em{E}val-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and {SVM}",
    author = "Mohammadi, Elham  and
      Amini, Hessam  and
      Kosseim, Leila",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2023/",
    doi = "10.18653/v1/S19-2023",
    pages = "153--158",
    abstract = "This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12{\%}."
}