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An ASP-based Approach to Answering Natural Language Questions for Texts

Published online by Cambridge University Press:  04 February 2022

DHRUVA PENDHARKAR ,
KINJAL BASU Open the ORCID record for KINJAL BASU [Opens in a new window] ,
FARHAD SHAKERIN  and
GOPAL GUPTA
DHRUVA PENDHARKAR
Affiliation:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA (e-mails: [email protected], [email protected], [email protected], [email protected])
KINJAL BASU
Affiliation:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA (e-mails: [email protected], [email protected], [email protected], [email protected])
FARHAD SHAKERIN
Affiliation:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA (e-mails: [email protected], [email protected], [email protected], [email protected])
GOPAL GUPTA
Affiliation:
Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA (e-mails: [email protected], [email protected], [email protected], [email protected])
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Abstract

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language texts. Knowledge in a text is modeled using a Neo Davidsonian-like formalism, which is then represented as an answer set program. Relevant commonsense knowledge is additionally imported from resources such as WordNet and represented in ASP. The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system. This approach can facilitate many natural language tasks such as automated question answering, text summarization, and automated question generation. ASP-based representation of techniques such as default reasoning, hierarchical knowledge organization, preferences over defaults, etc., are used to model commonsense reasoning methods required to accomplish these tasks. In this paper, we describe the CASPR system that we have developed to automate the task of answering natural language questions given English text. CASPR can be regarded as a system that answers questions by “understanding” the text and has been tested on the SQuAD data set, with promising results.

Keywords

ASPcommonsense reasoningNLPKRnatural language question answering
Type
Rapid Communication
Information
Theory and Practice of Logic Programming , Volume 22 , Issue 3 , May 2022 , pp. 419 - 443
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Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Alviano, M., Faber, W., Leone, N., Perri, S., Pfeifer, G. and Terracina, G. 2011. The disjunctive datalog system dlv. In Datalog Reloaded. Springer, 282–301.Google Scholar
Arias, J., Carro, M., Salazar, E., Marple, K. and Gupta, G. 2018. Constraint answer set programming without grounding. arXiv preprint arXiv:1804.11162.CrossRefGoogle Scholar
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R. and Ives, Z. 2007. Dbpedia: A nucleus for a web of open data. In The Semantic Web. Springer, 722–735.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.CrossRefGoogle Scholar
Baral, C. and SchÜller, P., Eds. 2013. Proceedings of the 1st Workshop on Natural Language Processing and Automated Reasoning 2013. CEUR Workshop Proceedings, vol. 1044. CEUR-WS.org.Google Scholar
Basu, K., Shakerin, F. and Gupta, G. 2020. AQuA: ASP-based visual question answering. In International Symposium on Practical Aspects of Declarative Languages. Springer, 57–72.Google Scholar
Basu, K., Varanasi, S., Shakerin, F., Arias, J. and Gupta, G. 2021. Knowledge-driven natural language understanding of english text and its applications. arXiv preprint arXiv:2101.11707.Google Scholar
Basu, K., Varanasi, S. C., Shakerin, F. and Gupta, G. 2020. Square: Semantics-based question answering and reasoning engine. arXiv preprint arXiv:2009.10239.CrossRefGoogle Scholar
Chen, Z., Marple, K., Salazar, E., Gupta, G. and Tamil, L. 2016. A physician advisory system for chronic heart failure management based on knowledge patterns. Theory and Practice of Logic Programming 16, 5–6, 604–618.CrossRefGoogle Scholar
Clark, P., Dalvi, B. and Tandon, N. 2018. What happened? leveraging verbnet to predict the effects of actions in procedural text. arXiv preprint arXiv:1804.05435.Google Scholar
Costantini, S. and Paolucci, A. 2010. Towards translating natural language sentences into asp. In CILC.Google Scholar
Davidson, D. 1984. Inquiries into Truth and Interpretation. Oxford University Press.Google Scholar
De Marneffe, M.-C., Dozat, T., Silveira, N., Haverinen, K., Ginter, F., Nivre, J. and Manning, C. D. 2014. Universal stanford dependencies: A cross-linguistic typology. In LREC. Vol. 14, 4585–4592.Google Scholar
De Marneffe, M.-C. and Manning, C. D. 2008. Stanford typed dependencies manual. Tech. rep., Technical report, Stanford University.Google Scholar
Finkel, J. R., Grenager, T. and Manning, C. 2005. Incorporating non-local information into information extraction systems by Gibbs sampling. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 363–370.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Thiele, S. 2010. gringo, clasp, clingo, and iclingo. User guide.Google Scholar
Gelfond, M. and Kahl, Y. 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach. Cambridge University Press.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In ICLP/SLP. Vol. 88, 1070–1080.Google Scholar
Jonson-Laird, P. 2009. How We Reason. Oxford University Press.Google Scholar
Jurafsky, D. 2000. Speech & Language Processing. Pearson Education India.Google Scholar
Kipper, K., Korhonen, A., Ryant, N. and Palmer, M. 2006. Extending verbnet with novel verb classes. In Proceedings of the Fifth International Conference on Language Resources and Evaluation, LREC 2006, Genoa, Italy, May 22–28, 2006, 1027–1032.Google Scholar
Mahdisoltani, F., Biega, J. and Suchanek, F. M. 2015. YAGO3: A knowledge base from multilingual wikipedias. In CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4-7, 2015, Online Proceedings.Google Scholar
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J. and McClosky, D. 2014. The Stanford CoreNLP natural language processing toolkit. In ACL System Demonstrations, 55–60.Google Scholar
Marple, K., Salazar, E. and Gupta, G. 2017. Computing stable models of normal logic programs without grounding. arXiv preprint arXiv:1709.00501.Google Scholar
Miller, G. A. 1995. WordNet: A lexical database for english. Communications of the ACM 38, 11, 39–41.CrossRefGoogle Scholar
Mitchell, T., Cohen, W., Hruschka, E., Talukdar, P., Betteridge, J., Carlson, A., Dalvi, B., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E., Ritter, A., Samadi, M., Settles, B., Wang, R., Wijaya, D., Gupta, A., Chen, X., Saparov, A., Greaves, M. and Welling, J. 2015. Never-ending learning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15).Google Scholar
Olson, C. and Lierler, Y. 2019. Information extraction tool Text2ALM: From narratives to action language system descriptions. Electronic Proceedings in Theoretical Computer Science 306, 87–100.CrossRefGoogle Scholar
Pendharkar, D. 2018a. An Answer Set Programming based Approach to Representing and Querying Textual Knowledge. M.S. Thesis, The University of Texas at Dallas, http://utdallas.edu/ gupta/dpthesis.pdf.Google Scholar
Pendharkar, D. 2018b. Caspr. https://github.com/DhruvaPendharkar/thesis-project.Google Scholar
Rajpurkar, P., Zhang, J., Lopyrev, K. and Liang, P. 2016. SQuAD: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250.Google Scholar
Schuster, S. and Manning, C. D. 2016. Enhanced english universal dependencies: An improved representation for natural language understanding tasks. In LRED’16, 2371–2378.Google Scholar
Vo, N. H., Mitra, A. and Baral, C. 2015. The NL2KR platform for building natural language translation systems. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, 899–908.Google Scholar
VrandeČiĆ, D. and KrÖtzsch, M. 2014. Wikidata: A free collaborative knowledgebase. Communications of the ACM 57, 10, 78–85.CrossRefGoogle Scholar
Wikipedia contributors. 2018. Cyc — Wikipedia, the free encyclopedia. [Accessed on May 17, 2018].Google Scholar
Zhang, Q., Benton, C. and Inclezan, D. 2020. An application of ASP theories of intentions to understanding restaurant scenarios: Insights and narrative corpus. Theory and Practice of Logic Programming 20, 2, 273–293.CrossRefGoogle Scholar