Iterative method of generating artificial context-free grammars

TL;DR

This paper introduces an iterative approach to generate artificial context-free grammars along with their optimal learning sets, providing a valuable benchmarking tool for evaluating grammar inference algorithms.

Contribution

It presents a novel iterative method for creating artificial context-free grammars and their optimal learning sets, aiding in benchmarking empirical grammar inference methods.

Findings

Provides a systematic way to generate grammars and learning sets

Facilitates benchmarking of grammar inference algorithms

Enhances testing with artificial, controlled data

Abstract

Grammatical inference is a machine learning area, whose fundamentals are built around learning sets. At present, real-life data and examples from manually crafted grammars are used to test their learning performance. This paper aims to present a method of generating artificial context-free grammars with their optimal learning sets, which could be successfully applied as a benchmarking tool for empirical grammar inference methods.