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

Published online by Cambridge University Press:  25 July 2023

TRUNG HOANG LE Open the ORCID record for TRUNG HOANG LE [Opens in a new window] ,
HUIPING CAO Open the ORCID record for HUIPING CAO [Opens in a new window]  and
TRAN CAO SON Open the ORCID record for TRAN CAO SON [Opens in a new window]
TRUNG HOANG LE
Affiliation:
Department of Computer Science, New Mexico State University, Las Cruces, NM, USA (e-mails: trungle@nmsu.edu, hcao@nmsu.edu, tson@cs.nmsu.edu)
HUIPING CAO
Affiliation:
Department of Computer Science, New Mexico State University, Las Cruces, NM, USA (e-mails: trungle@nmsu.edu, hcao@nmsu.edu, tson@cs.nmsu.edu)
TRAN CAO SON
Affiliation:
Department of Computer Science, New Mexico State University, Las Cruces, NM, USA (e-mails: trungle@nmsu.edu, hcao@nmsu.edu, tson@cs.nmsu.edu)
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Abstract

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.

Keywords

joint entity-relation extractionsemi-supervised learninganswer set programmingknowledge-enhanced models

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 765 - 781
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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Footnotes

*

Research partially funded by NSF awards #1757207, #1914635, and #1812628.

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