@inproceedings{passon-etal-2018-predicting,
    title = "Predicting the Usefulness of {A}mazon Reviews Using Off-The-Shelf Argumentation Mining",
    author = "Passon, Marco  and
      Lippi, Marco  and
      Serra, Giuseppe  and
      Tasso, Carlo",
    editor = "Slonim, Noam  and
      Aharonov, Ranit",
    booktitle = "Proceedings of the 5th Workshop on Argument Mining",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-5205/",
    doi = "10.18653/v1/W18-5205",
    pages = "35--39",
    abstract = "Internet users generate content at unprecedented rates. Building intelligent systems capable of discriminating useful content within this ocean of information is thus becoming a urgent need. In this paper, we aim to predict the usefulness of Amazon reviews, and to do this we exploit features coming from an off-the-shelf argumentation mining system. We argue that the usefulness of a review, in fact, is strictly related to its argumentative content, whereas the use of an already trained system avoids the costly need of relabeling a novel dataset. Results obtained on a large publicly available corpus support this hypothesis."
}