Learning SECp Languages from Only Positive Data

Leonor Becerra-Bonache

Abstract

The eld of Grammatical Inference provides a good theoretical framework for investigating a learning process. Formal results in this eld can be relevant to the question of rst language acquisition. However, Grammatical Inference studies have been focused mainly on mathematical aspects, and have not exploited the linguistic relevance of their results. With this paper, we try to enrich Grammatical Inference studies with ideas from Linguistics. We propose a non-classical mechanism that has relevant linguistic and computational properties, and we study its learnability from positive data.

Keywords

language; literature; computation

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