Kernel methods for learning languages

This paper studies a novel paradigm for learning formal languages from positive and negative examples which consists of mapping strings to an appropriate high-dimensional feature space and learning a separating hyperplane in that space. Such mappings can often be represented flexibly with string kernels, with the additional benefit of computational efficiency. The paradigm inspected can thus be viewed as that of using kernel methods for learning languages. We initiate the study of the linear separability of automata and languages by examining the rich class of piecewise-testable languages. We introduce a subsequence feature mapping to a Hilbert space and prove that piecewise-testable languages are linearly separable in that space. The proof makes use of word combinatorial results relating to subsequences. We also show that the positive definite symmetric kernel associated to this embedding is a rational kernel and show that it can be computed in quadratic time using general-purpose weighted automata algorithms. Our examination of the linear separability of piecewise-testable languages leads us to study the general problem of separability with other finite regular covers. We show that all languages linearly separable under a regular finite cover embedding, a generalization of the subsequence embedding we use, are regular. We give a general analysis of the use of support vector machines in combination with kernels to determine a separating hyperplane for languages and study the corresponding learning guarantees. Our analysis includes several additional linear separability results in abstract settings and partial characterizations for the linear separability of the family of all regular languages.