Iterative method of generating artificial context-free grammars [article]

Olgierd Unold, Agnieszka Kaczmarek, Łukasz Culer
<span title="2019-11-13">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="">arXiv:1911.05801v1</a> <a target="_blank" rel="external noopener" href="">fatcat:37nlzdrczvgijezrweihwpp7qm</a> </span>
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