This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities are dependent on each other and bound by the rules of the language, in the review. We observe that their inter-dependencies can be captured well using LSTMs. We evaluate our system on existing manually labeled datasets and observe out-performance over the existing Semantic Role Labeling (SRL) framework popular for this task.

翻译：本文提出一种基于深度学习的方法，用于从各类电商网站的用户评论中提取产品对比信息。任何对比性产品评论都包含三个核心信息实体：被比较产品的名称、用户观点（谓词）以及被比较的特征或方面。这些信息实体相互依存，并受评论语言规则的约束。我们观察到，利用长短期记忆网络（LSTM）可以很好地捕捉这些实体间的相互依赖关系。我们在现有手动标注数据集上对该系统进行了评估，发现其在当前流行的语义角色标注（SRL）框架基础上表现更优。