Controlled Natural Language Processing as Answer Set Programming: an Experiment

TL;DR

This paper explores using answer set programming as a unified framework for parsing controlled natural language, transforming input tokens into syntax trees and reasoning with derived facts through ASP-based methods.

Contribution

It demonstrates how ASP can be employed to parse CNL, generate formal syntax representations, and perform reasoning within a single unified framework.

Findings

ASP effectively unifies parsing and reasoning for CNL.

Transformations from tokens to syntax trees are feasible with ASP.

ASP-based reasoning can infer new knowledge from formal representations.

Abstract

Most controlled natural languages (CNLs) are processed with the help of a pipeline architecture that relies on different software components. We investigate in this paper in an experimental way how well answer set programming (ASP) is suited as a unifying framework for parsing a CNL, deriving a formal representation for the resulting syntax trees, and for reasoning with that representation. We start from a list of input tokens in ASP notation and show how this input can be transformed into a syntax tree using an ASP grammar and then into reified ASP rules in form of a set of facts. These facts are then processed by an ASP meta-interpreter that allows us to infer new knowledge.