Correction Queries and Language Learning
Language as a Complex System: Interdisciplinary Approaches
Cambridge Scholars Publishing
In the field of grammatical inference, the goal of any learning algorithm is to identify (or to output a good enough approximation of) a target concept from a given class by having access to a specific type of information.
We thoroughly investigate a recently introduced, linguistic motivated, type of query called Correction Query (CQ). We consider three possible definitions, and for each of them we give necessary and sufficient conditions for a language
class to be learnable with these types of queries. Furthermore, we compare the model of learning with CQs with other well-known Gold-style and query learning models when no efficiency constraints are imposed. Results are also obtained
for the restricted version of the model of learning with CQs in polynomial time.
Additionally, we discuss the learnability of deterministic finite automata with correction and equivalence queries. We design several learning algorithms and we present a comparison between our algorithms and the standard L∗ algorithm for learning DFAs with membership and equivalence queries.
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