Distantly Supervised Attribute Detection from Reviews

Lisheng Fu, Pablo Barrio


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
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
74–78
Language:
URL:
https://www.aclweb.org/anthology/W18-6110
DOI:
10.18653/v1/W18-6110
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6110.pdf