@inproceedings{fu-barrio-2018-distantly,
    title = "Distantly Supervised Attribute Detection from Reviews",
    author = "Fu, Lisheng  and
      Barrio, Pablo",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 2018 {EMNLP} Workshop W-{NUT}: The 4th Workshop on Noisy User-generated Text",
    month = nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W18-6110/",
    doi = "10.18653/v1/W18-6110",
    pages = "74--78",
    abstract = "This work aims to detect specific attributes of a place (e.g., if it has a romantic atmosphere, or if it offers outdoor seating) from its user reviews via distant supervision: without direct annotation of the review text, we use the crowdsourced attribute labels of the place as labels of the review text. We then use review-level attention to pay more attention to those reviews related to the attributes. The experimental results show that our attention-based model predicts attributes for places from reviews with over 98{\%} accuracy. The attention weights assigned to each review provide explanation of capturing relevant reviews."
}