Learning Constraint Grammars

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.

Papers

• http://swarm.cs.pub.ro/~asfrent/msc/thesis.pdf – Inductive Logic Programming
• http://ucrel.lancs.ac.uk/acl/C/C98/C98-2123.pdf – Inductive Logic Programming