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An ASP Based Approach to Answering Questions for Natural Language Text

  • Dhruva PendharkarEmail author
  • Gopal GuptaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11372)

Abstract

An approach based on answer set programming (ASP) is proposed in this paper for representing knowledge generated from natural language text. Knowledge in the text is modeled using a Neo Davidsonian-like formalism, represented as an answer set program. Relevant common sense 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 common-sense 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

ASP Common sense reasoning NLP KR 

Notes

Acknowledgment

Authors thank NSF (Grant IIS 1718945) and members of their research group (Zhuo Chen, Farhad Shakerin, Elmer Salazar, Joaquin Arias, Sarat Varanasi, Kyle Marple).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Texas at DallasRichardsonUSA

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