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A plethora of approaches have been proposed for joint entity-relation (ER)extraction. Most of these methods largely depend on a large amount of manuallyannotated training data. However, manual data annotation is time consuming,labor intensive, and error prone. Human beings learn using both data (throughinduction) and knowledge (through deduction). Answer Set Programming (ASP) hasbeen a widely utilized approach for knowledge representation and reasoning thatis 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 dataand domain knowledge. In particular, ASPER takes advantage of the factualknowledge (represented as facts in ASP) and derived knowledge (represented asrules in ASP) in the learning process of neural network models. We haveconducted experiments on two real datasets and compare our method with threebaselines. The results show that our ASPER model consistently outperforms thebaselines.

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction