Difference between revisions of "Tagger"
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The program <code>apertium-tagger</code> achieves this by using a Hidden Markov Model, a statistical model using bigrams (trigram training is also possible). Training of <code>apertium-tagger</code> can be supervised or unsupervised; there is also [[target-language |
The program <code>apertium-tagger</code> achieves this by using a Hidden Markov Model, a statistical model using bigrams (trigram training is also possible). Training of <code>apertium-tagger</code> can be supervised or unsupervised; there is also [[target-language tagger training]] where training is based on how good the translations given by the tagging are, using a target-language language model. If a certain bigram sequence is impossible, one may explicitly tell the tagger this with FORBID or ENFORCE rules. |
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Some language pairs use [[Constraint Grammar]] (CG) to remove more readings before <code>apertium-tagger</code>; CG lets you write rule-based taggers which allows more complex rules. |
Some language pairs use [[Constraint Grammar]] (CG) to remove more readings before <code>apertium-tagger</code>; CG lets you write rule-based taggers which allows more complex rules. |
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Revision as of 07:20, 24 March 2010
Tagger is usually short for part-of-speech tagger, a program which takes an ambiguous sequence of morphologically analysed text and chooses the most probable analysis.
Given the following ambiguous input (from "tengo una idea")
^tengo/tener<vblex><pri><p1><sg>$ ^una/uno<prn><tn><f><sg>/uno<det><ind><f><sg>/unir<vblex><prs><p3><sg>/unir<vblex><prs><p1><sg>/unir<vblex><imp><p3><sg>$ ^idea/idea<n><f><sg>/idear<vblex><pri><p3><sg>/idear<vblex><imp><p2><sg>$
a good tagger would end up with
^tener<vblex><pri><p1><sg>$ ^uno<det><ind><f><sg>$ ^idea<n><f><sg>$
The program apertium-tagger achieves this by using a Hidden Markov Model, a statistical model using bigrams (trigram training is also possible). Training of apertium-tagger can be supervised or unsupervised; there is also target-language tagger training where training is based on how good the translations given by the tagging are, using a target-language language model. If a certain bigram sequence is impossible, one may explicitly tell the tagger this with FORBID or ENFORCE rules.
Some language pairs use Constraint Grammar (CG) to remove more readings before apertium-tagger; CG lets you write rule-based taggers which allows more complex rules.
