Difference between revisions of "Comparison of part-of-speech tagging systems"
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* <code>Unigram</code>: Lexicalised unigram tagger |
* <code>Unigram</code>: Lexicalised unigram tagger |
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* <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. |
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==Todo== |
==Todo== |
Revision as of 21:30, 21 December 2015
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 analyserCG
: Uses the CG (from the monolingual language package in languages) to preprocess the input.Unigram
: Lexicalised unigram taggerapertium-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
- Implement this tagger: https://spacy.io/blog/part-of-speech-POS-tagger-in-python