  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.
