联合识别和修复信息抽取系统中的不一致读数
Jointly Identifying and Fixing Inconsistent Readings from Information Extraction Systems
摘要：
KGCleaner是一个框架，用于识别和纠正由信息抽取系统产生和传递的数据中的错误。这些任务已经被充分研究，KGCleaner是第一个解决这两个问题的。我们引入了一个多任务模型，该模型联合学习预测提取的关系是否可信，如果不可信则修复它。我们评估了我们的方法和其他模型，作为我们框架的两个集合的实例：一个包含近70万事实和500万事实相关句子的维基数据语料库，以及一个来自2015年TAC知识库人口任务的3万事实的集合。对于可信度分类，参数有效的简单浅层神经网络可以在Wikidata上实现30个$F_1$点的绝对性能增益，并且在TAC上具有相当的性能。对于修复任务，可以根据数据集和模型的性质获得显着的性能（超过两倍）增益。
英文摘要：
KGCleaner is a framework to identify and correct errors in data produced and delivered by an information extraction system. These tasks have been understudied and KGCleaner is the first to address both. We introduce a multi-task model that jointly learns to predict if an extracted relation is credible and repair it if not. We evaluate our approach and other models as instance of our framework on two collections: a Wikidata corpus of nearly 700K facts and 5M fact-relevant sentences and a collection of 30K facts from the 2015 TAC Knowledge Base Population task. For credibility classification, parameter efficient simple shallow neural network can achieve an absolute performance gain of 30 $F_1$ points on Wikidata and comparable performance on TAC. For the repair task, significant performance (at more than twice) gain can be obtained depending on the nature of the dataset and the models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
