Comparison of part-of-speech tagging systems
Revision as of 16:39, 21 December 2015 by Francis Tyers (talk | contribs)
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] |
Kazakh | 403 | 4,348 | 80.25 | 86.13 | [83.55, 86.19] | [83.33, 86.61] | n/a | n/a |
Todo
- Implement this tagger: https://spacy.io/blog/part-of-speech-POS-tagger-in-python