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.- Anthology ID:
- W18-6110
- Volume:
- Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
- Month:
- November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- WNUT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–78
- Language:
- URL:
- https://aclanthology.org/W18-6110
- DOI:
- 10.18653/v1/W18-6110
- Cite (ACL):
- Lisheng Fu and Pablo Barrio. 2018. Distantly Supervised Attribute Detection from Reviews. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 74–78, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Distantly Supervised Attribute Detection from Reviews (Fu & Barrio, WNUT 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-6110.pdf