Tagger - Revision history

ScoopGracie at 01:59, 24 January 2020

2020-01-24T01:59:39Z

Bech: Link to French page

2014-10-07T12:24:26Z

Link to French page

Bech: Category:Documentation in English

2012-03-24T10:44:26Z

Category:Documentation in English

Unhammer at 07:20, 24 March 2010

2010-03-24T07:20:31Z

Unhammer at 07:20, 24 March 2010

2010-03-24T07:20:05Z

Francis Tyers: Created page with ''''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. ==S…'

2010-03-12T11:29:20Z

Created page with ''''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. ==S…'

New page

'''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.

==See also==

* [[Tagger training]]
* [[Constraint Grammar]]

[[Category:Terminology]]

← Older revision		Revision as of 01:59, 24 January 2020
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	[[Tagger (français)\|~~En français]]~~		{{otherlang\|Tagger (français)\|{{french}}}}

	'''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.		'''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.

← Older revision		Revision as of 12:24, 7 October 2014
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			[[Tagger (français)\|En français]]

	'''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.		'''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.

← Older revision		Revision as of 10:44, 24 March 2012
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	[[Category:Terminology]]		[[Category:Terminology]]
			[[Category:Documentation in English]]

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 </pre>
-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 target 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.
+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.
 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.

@@ Line 1: / Line 1: @@
 '''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")
+<pre>
+^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>$
+</pre>
+a good tagger would end up with
+<pre>
+^tener<vblex><pri><p1><sg>$ ^uno<det><ind><f><sg>$ ^idea<n><f><sg>$
+</pre>
+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 target 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 <code>apertium-tagger</code>; CG lets you write rule-based taggers which allows more complex rules.
 ==See also==