Timeline for What machine learning algorithms to use for unsupervised POS tagging?
Current License: CC BY-SA 4.0
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| Sep 23, 2021 at 1:45 | history | edited | Ethan | CC BY-SA 4.0 |
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| S Aug 7, 2020 at 9:27 | history | suggested | Zephyr | CC BY-SA 4.0 |
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| Aug 7, 2020 at 8:44 | review | Suggested edits | |||
| S Aug 7, 2020 at 9:27 | |||||
| Jul 18, 2018 at 16:47 | comment | added | MkL | Snowball is a stemmer so it does not produce lemma (base form) but a kind of 'simplified form' common to all inflected forms. That can lead to a non-existing forms like 'company' -> 'compani but it's enough to have a common form - common dictionary identifier for all inflected forms - for further processing, e.g. text classification. But it may be a bit awkward when you want to visualize results. Having looked to Snowball for German, it does the same - tries to cut off typical suffixes for conjugation and declination et al. E.g. Hunde -> Hund but also Katze, Katzen -> Katz. | |
| Jul 17, 2018 at 18:38 | comment | added | Tido | like for NER. But indeed, I wanted to use POSs for a lemmatizer: github.com/WZBSocialScienceCenter/germalemma and spaCy's lemmatizer for german is very bad. Do you know about the accuarcy of this stemmer? Sounds interesting. | |
| Jul 17, 2018 at 17:40 | history | answered | MkL | CC BY-SA 4.0 |