论文标题
从评论中构建可解释的意见图
Constructing Explainable Opinion Graphs from Review
论文作者
论文摘要
网络是事实和主观信息的主要资源。尽管将事实信息组织到知识库中有很多努力，但是在组织观点中，主观数据中丰富的观点的工作要少得多，为结构化的格式。 我们提供了解释，该系统将提取和组织意见为意见图的系统，对于下游应用程序，例如生成可解释的评论摘要以及促进对意见短语的搜索。在这样的图中，一个节点表示从评论中提取的一组语义上相似的观点，两个节点之间的边缘表示一个节点可以解释另一个节点。用监督方法解释地雷解释，并以弱监督的方式将类似的观点结合在一起，然后将意见集簇及其解释关系组合到意见图中。我们在实验上证明，观点图中产生的解释关系质量良好，我们的标签数据集用于解释挖掘和分组意见。
The Web is a major resource of both factual and subjective information. While there are significant efforts to organize factual information into knowledge bases, there is much less work on organizing opinions, which are abundant in subjective data, into a structured format. We present ExplainIt, a system that extracts and organizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. In such graphs, a node represents a set of semantically similar opinions extracted from reviews and an edge between two nodes signifies that one node explains the other. ExplainIt mines explanations in a supervised method and groups similar opinions together in a weakly supervised way before combining the clusters of opinions together with their explanation relationships into an opinion graph. We experimentally demonstrate that the explanation relationships generated in the opinion graph are of good quality and our labeled datasets for explanation mining and grouping opinions are publicly available.
