Abstract
Context Free Grammars (CFGs) are widely used in programming language descriptions, natural language processing, compilers, and other areas of software engineering where there is a need for describing the syntactic structures of programs. Grammar inference (GI) is the induction of CFGs from sample programs and is a challenging problem. We describe an unsupervised GI approach which uses simplicity as the criterion for directing the inference process and beam search for moving from a complex to a simpler grammar. We use several operators to modify a grammar and use the Minimum Description Length (MDL) Principle to favor simple and compact grammars. The effectiveness of this approach is shown by a case study of a domain specific language. The experimental results show that an accurate grammar can be inferred in a reasonable amount of time.
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Sapkota, U., Bryant, B.R., Sprague, A. (2012). Unsupervised Grammar Inference Using the Minimum Description Length Principle. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_12
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DOI: https://doi.org/10.1007/978-3-642-31537-4_12
Publisher Name: Springer, Berlin, Heidelberg
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