Distantly Supervised Attribute Detection from Reviews

NUT@EMNLP Pub Date : 2018-11-01 DOI:10.18653/v1/W18-6110
Lisheng Fu, Pablo Barrio
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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.
远程监督属性检测从评论
这项工作旨在通过远程监督从用户评论中检测一个地方的特定属性(例如,它是否有浪漫的氛围,或者它是否提供户外座位):在不直接注释评论文本的情况下,我们使用该地方的众包属性标签作为评论文本的标签。然后,我们使用审查级别的关注来更多地关注那些与属性相关的审查。实验结果表明,我们的基于注意力的模型从评论中预测地点属性的准确率超过98%。分配给每个审查的注意权重提供了捕获相关审查的解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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