论文标题
一种知识驱动的方法，用于分类对象和属性核心发作
A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining
论文作者
论文摘要
对对象（例如，产品名称）和属性（例如产品方面）进行分类和解决的核心发挥对于改善意见挖掘绩效至关重要。但是，这项任务具有挑战性，因为人们通常需要考虑特定领域的知识（例如，iPad是平板电脑，具有方面解决方案）来识别有思想评论中的核心发挥。此外，编译每个域的手工制作和精选的领域特定的知识库非常耗时且艰巨。本文提出了一种方法，可以自动挖掘和利用特定领域的知识来分类对象和属性核心发挥。该方法从未标记的审核数据中提取特定于领域的知识，并训练知识软件神经核心分类模型，以利用（有用的）域知识以及一般的常识知识。对涉及五个域（产品类型）的REALWORLD数据集进行的实验评估显示了该方法的有效性。
Classifying and resolving coreferences of objects (e.g., product names) and attributes (e.g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance. However, the task is challenging as one often needs to consider domain-specific knowledge (e.g., iPad is a tablet and has aspect resolution) to identify coreferences in opinionated reviews. Also, compiling a handcrafted and curated domain-specific knowledge base for each domain is very time consuming and arduous. This paper proposes an approach to automatically mine and leverage domain-specific knowledge for classifying objects and attribute coreferences. The approach extracts domain-specific knowledge from unlabeled review data and trains a knowledgeaware neural coreference classification model to leverage (useful) domain knowledge together with general commonsense knowledge for the task. Experimental evaluation on realworld datasets involving five domains (product types) shows the effectiveness of the approach.
