Learning Cover Context-Free Grammars from Structural Data
- Cite this paper as:
- Marin M., Istrate G. (2014) Learning Cover Context-Free Grammars from Structural Data. In: Ciobanu G., Méry D. (eds) Theoretical Aspects of Computing – ICTAC 2014. ICTAC 2014. Lecture Notes in Computer Science, vol 8687. Springer, Cham
We consider the problem of learning an unknown context-free grammar when the only knowledge available and of interest to the learner is about its structural descriptions with depth at most ℓ. The goal is to learn a cover context-free grammar (CCFG) with respect to ℓ, that is, a CFG whose structural descriptions with depth at most ℓ agree with those of the unknown CFG. We propose an algorithm, called LAℓ, that efficiently learns a CCFG using two types of queries: structural equivalence and structural membership. We show that LAℓ runs in time polynomial in the number of states of a minimal deterministic finite cover tree automaton (DCTA) with respect to ℓ. This number is often much smaller than the number of states of a minimum deterministic finite tree automaton for the structural descriptions of the unknown grammar.
Keywordsautomata theory and formal languages structural descriptions grammatical inference
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