Difference between revisions of "Learning Constraint Grammars"
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== Statistical approach == |
== Statistical approach == |
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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 [https://github.com/nuboro/ |
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 [https://github.com/nuboro/CG-generator nuboro's Github repository], and it is based on the paper [https://archive.org/details/arxiv-cmp-lg9607002 Inducing Constraint Grammars]. |
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== Machine Learning == |
== Machine Learning == |
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* http://swarm.cs.pub.ro/~asfrent/msc/thesis.pdf – Inductive Logic Programming |
* http://swarm.cs.pub.ro/~asfrent/msc/thesis.pdf – Inductive Logic Programming |
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* http://ucrel.lancs.ac.uk/acl/C/C98/C98-2123.pdf – Inductive Logic Programming |
* http://ucrel.lancs.ac.uk/acl/C/C98/C98-2123.pdf – Inductive Logic Programming |
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[[Category:Constraint Grammar]] |
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Latest revision as of 22:59, 25 December 2016
Constraint Grammar style part-of-speech disambiguation rules can be learned automatically from disambiguated parallel corpora.
Statistical approach[edit]
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[edit]
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[edit]
- 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
