A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines.

翻译：针对联合实体关系抽取，已有大量方法被提出。然而，这些方法大多严重依赖大量人工标注的训练数据，而人工标注耗时耗力且易出错。人类通过数据（归纳）和知识（演绎）两种途径进行学习。回答集程序作为一种广泛使用的知识表示与推理方法，具有容错性强、善于处理不完整信息的优势。本文提出一种新方法——ASP增强的实体关系抽取（ASPER），通过同时学习数据与领域知识实现实体与关系的联合识别。具体而言，ASPER在神经网络模型的学习过程中融合了事实知识（以ASP事实表示）与派生知识（以ASP规则表示）。我们在两个真实数据集上开展实验，并与三种基线方法进行对比。结果表明，我们的ASPER模型在性能上始终优于基线方法。