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Comparison of part-of-speech tagging systems

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| Kazakh || 403 || 4,348 || 80.25 || 86.13 || [83.55, 86.19] || [83.33, 86.61] || n/a || n/a
 
| Kazakh || 403 || 4,348 || 80.25 || 86.13 || [83.55, 86.19] || [83.33, 86.61] || n/a || n/a
 
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| Russian || 451 || 10,171 || || || [56.13, 62.65] || [65.09, 70.87] || n/a || n/a
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| Russian || 451 || 10,171 || 70.12 || 72.78 || [56.13, 62.65] || [65.09, 70.87] || n/a || n/a
 
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Revision as of 23:05, 21 December 2015

Contents

Apertium would like to have really good part-of-speech tagging, but in many cases falls below the state-of-the-art (around 97% tagging accuracy). This page intends to collect a comparison of tagging systems in Apertium and give some ideas of what could be done to improve them.

In the following table, the intervals represent the [low, high] values from 10-fold cross validation.

Language Corpus System
Sent Tok 1st CG+1st Unigram CG+Unigram apertium-tagger CG+apertium-tagger
Catalan 1,413 24,144 81.85 83.96 [75.65, 78.46] [87.76, 90.48] [94.16, 96.28] [93.92, 96.16]
Spanish 1,271 21,247 86.18 86.71 [78.20, 80.06] [87.72, 90.27] [90.15, 94.86] [91.84, 93.70]
Serbo-Croatian 1,190 20,128 75.22 79.67 [75.36, 78.79] [75.36, 77.28]
Kazakh 403 4,348 80.25 86.13 [83.55, 86.19] [83.33, 86.61] n/a n/a
Russian 451 10,171 70.12 72.78 [56.13, 62.65] [65.09, 70.87] n/a n/a

Systems

  • 1st: Selects the first analysis from the morphological analyser
  • CG: Uses the CG (from the monolingual language package in languages) to preprocess the input.
  • Unigram: Lexicalised unigram tagger
  • apertium-tagger: Uses the bigram HMM tagger included with Apertium.

Corpora

The tagged corpora used in the experiments are found in the monolingual packages in languages, under the texts/ subdirectory.

Todo

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