@InProceedings{nguyen-dang-nguyen:2019:S19-2,
  author    = {Nguyen, Duc-Vu  and  Dang, Thin  and  Nguyen, Ngan},
  title     = {NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {971--975},
  abstract  = {This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.},
  url       = {http://www.aclweb.org/anthology/S19-2167}
}