@inproceedings{rissola-etal-2018-usi,
    title = "{USI}-{IR} at {IEST} 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection",
    author = "R{\'i}ssola, Esteban  and
      Giachanou, Anastasia  and
      Crestani, Fabio",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      Hoste, Veronique  and
      Klinger, Roman",
    booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-6233/",
    doi = "10.18653/v1/W18-6233",
    pages = "231--234",
    abstract = "This paper describes the participation of USI-IR in WASSA 2018 Implicit Emotion Shared Task. We propose a relevance feedback approach employing a sequential model (biLSTM) and word embeddings derived from a large collection of tweets. To this end, we assume that the top-\textit{k} predictions produce at a first classification step are correct (based on the model accuracy) and use them as new examples to re-train the network."
}