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Comparison of part-of-speech tagging systems

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* <code>Unigram</code>: Lexicalised unigram tagger
 
* <code>Unigram</code>: Lexicalised unigram tagger
 
* <code>apertium-tagger</code>: Uses the bigram HMM tagger included with Apertium.
 
* <code>apertium-tagger</code>: Uses the bigram HMM tagger included with Apertium.
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==Corpora==
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The tagged corpora used in the experiments are found in the monolingual packages in [[languages]], under the <code>texts/</code> subdirectory.
   
 
==Todo==
 
==Todo==

Revision as of 22:30, 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

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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