Learning Constraint Grammars

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Constraint Grammar style part-of-speech disambiguation rules can be learned automatically from disambiguated parallel corpora.

Statistical approach

In statistical approach Constraint Grammar style rules are learned by calculating n-gram probabilities of word and part-of-speech tag groups. Current work on implementing such a system is at nuboro's Github repository, and it is based on the paper Inducing Constraint Grammars.

Machine Learning

A subfield of Machine Learning, called Inductive Logic Programming, has been used to learn Constraint Grammar style disambiguation rules. See for example the branch mil-pos-tagger