Hostname: page-component-76c49bb84f-l6pkv Total loading time: 0 Render date: 2025-07-05T08:20:23.295Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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
Check for updates
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

*

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

References

Bengio, Y., Louradour, J., Collobert, R. and Weston, J. 2009. Curriculum learning. In ICML, vol. 382. ACM, 41–48.Google Scholar
Cai, L., Dai, W., Huang, Y., Li, Y., Muggleton, S. H. and Jiang, Y. 2021. Abductive learning with ground knowledge base. In IJCAI. ijcai.org, 1815–1821.Google Scholar
Cascante-Bonilla, P., Tan, F., Qi, Y. and Ordonez, V. 2021. Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning. In AAAI. AAAI Press, 6912–6920.Google Scholar
Chapelle, O., Schölkopf, B. and Zien, A., Eds. 2006. Semi-Supervised Learning. The MIT Press.10.7551/mitpress/9780262033589.001.0001CrossRefGoogle Scholar
Chen, M., Zhao, Y., He, B., Han, Z., Wu, B. and Yao, J. 2022. Learning with noisy labels over imbalanced subpopulations. CoRR abs/2211.08722.Google Scholar
Dai, W., Xu, Q., Yu, Y. and Zhou, Z. 2019. Bridging machine learning and logical reasoning by abductive learning. In NeurIPS, 2811–2822.Google Scholar
d’Avila Garcez, A. S., Gori, M., Lamb, L. C., Serafini, L., Spranger, M. and Tran, S. N. 2019. Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. FLAP 6, 4, 611–632.Google Scholar
Eberts, M. and Ulges, A. 2020. Span-based joint entity and relation extraction with transformer pre-training. In ECAI 2020 - 24th European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 325. IOS Press, 2006–2013.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. In Technical Communications of the 13th ICLP, vol. 14(4-5).Google Scholar
Gelfond, M. and Lifschitz, V. 1990. Logic programs with classical negation. In Logic Programming: Proc. of the Seventh International Conference, Warren, D. and Szeredi, P., Eds., 579–597.Google Scholar
Gupta, P., Schütze, H. and Andrassy, B. 2016. Table filling multi-task recurrent neural network for joint entity and relation extraction. In COLING. ACL, 2537–2547.Google Scholar
Hu, X., Zhang, C., Ma, F., Liu, C., Wen, L. and Yu, P. S. 2021. Semi-supervised relation extraction via incremental meta self-training. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, 487–496.Google Scholar
Hu, Z., Ma, X., Liu, Z., Hovy, E. H. and Xing, E. P. 2016. Harnessing deep neural networks with logic rules. In ACL.10.18653/v1/P16-1228CrossRefGoogle Scholar
Huang, Y., Dai, W., Yang, J., Cai, L., Cheng, S., Huang, R., Li, Y. and Zhou, Z. 2020. Semi-supervised abductive learning and its application to theft judicial sentencing. In 20th IEEE International Conference on Data Mining, ICDM 2020. IEEE, 1070–1075.Google Scholar
Lifschitz, V. and Turner, H. 1994. Splitting a logic program. In Proceedings of the Eleventh International Conference on Logic Programming, P. Van Hentenryck, Ed., 23–38.Google Scholar
Luan, Y., He, L., Ostendorf, M. and Hajishirzi, H. 2018. Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In EMNLP, 3219–3232.Google Scholar
Marek, V. and Truszczyński, M. 1999. Stable models and an alternative logic programming paradigm. In The Logic Programming Paradigm: A 25-year Perspective, 375–398.Google Scholar
McClosky, D., Charniak, E. and Johnson, M. 2006. Effective self-training for parsing. In Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings. The Association for Computational Linguistics.10.3115/1220835.1220855CrossRefGoogle Scholar
Niemelä, I. 1999. Logic programming with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3,4, 241–273.Google Scholar
Ouali, Y., Hudelot, C. and Tami, M. 2020. An overview of deep semi-supervised learning. CoRR abs/2006.05278.Google Scholar
Reichart, R. and Rappoport, A. 2007. Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets. In ACL.Google Scholar
Roth, D. and Yih, W. 2004. A linear programming formulation for global inference in natural language tasks. In CoNLL. ACL, 1–8.Google Scholar
Ruder, S. and Plank, B. 2018. Strong baselines for neural semi-supervised learning under domain shift. In ACL 2018. Association for Computational Linguistics, 1044–1054.Google Scholar
Wang, J. and Lu, W. 2020. Two are better than one: Joint entity and relation extraction with table-sequence encoders. In (EMNLP), 1706–1721.10.18653/v1/2020.emnlp-main.133CrossRefGoogle Scholar
Yang, Z., Ishay, A. and Lee, J. 2020. Neurasp: Embracing neural networks into answer set programming. In IJCAI 2020, C. Bessiere, Ed. ijcai.org, 1755–1762.Google Scholar
Zhou, Z. 2019. Abductive learning: Towards bridging machine learning and logical reasoning. Science China Information Sciences 62, 7, 76101:1–76101:3.Google Scholar
Zhou, Z. and Li, M. 2005. Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17, 11, 1529–1541.10.1109/TKDE.2005.186CrossRefGoogle Scholar