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- CLT seminar: Måns Hulden - Grammatical Inference: learning finite state machines in computational morphology and phonology

SEMINAR

One the fundamental tasks of Grammatical Inference (GI) is the induction of a formal grammar that, on the basis of access to either only positive or both positive and negative examples, correctly characterizes (linguistic) data. The task is known to be computationally intractable in various incarnations, even when the grammar chosen is fairly limited in generative power, such as a finite-state automaton. However, recent heuristic and machine learning approaches have improved the picture somewhat: fairly large non-probabilistic and probabilistic automata can now be learned with some degree of success. These can both yield useful generalizations and be used in applications.

In this talk I present some recent results related to the grammatical inference of finite automata, discuss some practical applications related to phonology and morphology, and draw some connections with alternate approaches to inducing small grammars from data using Bayesian approaches like Minimum Description Length/Minimum Message Length.

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