A minimum description length approach to grammar inference

We describe a new abstract model for the computational learning of grammars. The model deals with a learning process in which an algorithm is given an input of a large set of training sentences that belong to some unknown grammar. The algorithm then tries to infer this grammar. Our model is based on the well-known Minimum Description Length Principle. It is quite close to, but more general than several other existing approaches. We have shown that one of these approaches (based on n-gram statistics) coincides exactly with a restricted version of our own model. We have used a restricted version of the algorithm implied by the model to nd classes of related words in natural language texts. It turns out that for this task, which can be seen as a `degenerate' case of grammar learning, our approach gives quite good results. As opposed to many other approaches, it also provides a clear`stopping criterion' indicating at what point the learning process should stop.