@InProceedings{rissola-giachanou-crestani:2018:WASSA2018,
  author    = {Rissola, Esteban  and  Giachanou, Anastasia  and  Crestani, Fabio},
  title     = {USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection},
  booktitle = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  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-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.},
  url       = {http://aclweb.org/anthology/W18-6233}
}