On the learnibility of Mildly Context-Sensitive languages using positive data and correction queries

Identificador:  TDX:559

Handle: https://hdl.handle.net/20.500.11797/TDX559

Autors:  Becerra Bonache, Leonor

Resum:
With this dissertation, we bring together the Theory of the Grammatical Inference and Studies of language acquisition, in pursuit of our final goal: to go deeper in the understanding of the process of language acquisition by using the theory of inference of formal grammars. Our main three contributions are:1. Introduction of a new class of languages called Simple p-dimensional external contextual (SEC). Despite the fact that the field of Grammatical Inference has focused its research on learning regular or context-free languages, we propose in our dissertation to focus these studies in classes of languages more relevant from a linguistic point of view (families of languages that occupy an orthogonal position in the Chomsky Hierarchy and are Mildly Context-Sensitive, for example SEC).2. Presentation of a new learning paradigm based on correction queries. One of the main results in the theory of formal learning is that deterministic finite automata (DFA) are efficiently learnable from membership query and equivalence query. Taken into account that in first language acquisition the correction of errors can play an important role, we have introduced in our dissertation a novel learning model by replacing membership queries with correction queries.3. Presentation of results based on the two previous contributions. First, we prove that SEC is learnable from only positive data. Second, we prove that it is possible to learn DFA from corrections and that the number of queries is reduced considerably.The results obtained with this dissertation suppose an important contribution to studies of Grammatical Inference (the current research in Grammatical Inference has focused mainly on the mathematical aspects of the models). Moreover, these results could be extended to studies related directly to machine translation, robotics, natural language processing, and bioinformatics.