There has been growing interest over the last few years in learning grammars from natural language text (and structured or semi-structured text). The family of techniques enabling such learning is usually called "grammatical inference" or "grammar induction".

The field of grammatical inference is often subdivided into formal grammatical inference, where researchers aim to proof efficient learnability of classes of grammars, and empirical grammatical inference, where the aim is to learn structure from data. In this case the existence of an underlying grammar is just regarded as a hypothesis and what is sought is to better describe the language through some automatically learned rules.

Both formal and empirical grammatical inference have been linked with (computational) linguistics. Formal learnability of grammars has been used in discussions on how people learn language. Some people mention proofs of (non-)learnability of certain classes of grammars as arguments in the empiricist/nativist discussion. On the more practical side, empirical systems that learn grammars have been applied to natural language. Instead of proving whether classes of grammars can be learnt, the aim here is to provide practical learning systems that automatically introduce structure in language. Example fields where initial research has been done are syntactic parsing, morphological analysis of words, and bilingual modeling (or machine translation).

This workshop at EACL 2009 aims to explore the state-of-the-art in these topics. In particular, we aim at bringing formal and empirical grammatical inference researchers closer together with researchers in the field of computational linguistics.