ASPER: Answer Set Programming Enhanced Neural Network Models for Joint
  Entity-Relation Extraction
Trung Hoang Le Huiping Cao Tran Cao Son
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.