Difference between revisions of "Learning Constraint Grammars"
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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/CG_module/ nuboro's Github repository], and it is based on the paper [https://archive.org/details/arxiv-cmp-lg9607002 Inducing Constraint Grammars]. |
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_module/ 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 == |
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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] |
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Revision as of 12:15, 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
