Controlled Natural Language Processing as Answer Set Programming: an Experiment
This work addresses the challenge of fragmented software pipelines in CNL processing for researchers and practitioners in computational linguistics, though it appears incremental as it applies an existing method (ASP) to a new domain.
The paper tackled the problem of processing controlled natural languages (CNLs) by exploring answer set programming (ASP) as a unified framework for parsing, representation, and reasoning, showing it can transform input tokens into syntax trees and reified ASP rules for knowledge inference.
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.