Stochastic Grammatical Inference of TextDatabase

For a document collection in which structural elements are identiied with markup, it is often necessary to construct a grammar retrospectively that constrains element nesting and ordering. This has been addressed by others as an application of grammatical inference. We describe an approach based on stochastic grammatical inference which scales more naturally to large data sets and produces models with richer semantics. We adopt an algorithm that produces stochastic nite automata and describe modiications that enable better interactive control of results. Our experimental evaluation uses four document collections with varying structure.