Difference between revisions of "Learning Constraint Grammars"
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== Machine Learning == |
== Machine Learning == |
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| − | A subfield of Machine Learning, called Inductive Logic Programming, has been used to learn Constraint Grammar style disambiguation rules. See for example the branch [http://svn.code.sf.net/p/apertium/svn/branches/mil-pos-tagger/ mil-pos-tagger] |
+ | A subfield of Machine Learning, called Inductive Logic Programming, has been used to learn Constraint Grammar style disambiguation rules. See for example the branch [http://svn.code.sf.net/p/apertium/svn/branches/mil-pos-tagger/ mil-pos-tagger]. |
==External links== |
==External links== |
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Revision as of 12:25, 22 December 2016
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.
External links
- 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
