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add requirement for morphologizer,tagger to rule-based lemmatizer

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svlandeg 2023-07-07 09:27:03 +02:00
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1 changed files with 2 additions and 2 deletions
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website/docs/usage/linguistic-features.mdx
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@ -170,7 +170,7 @@ nlp = spacy.blank("sv")
nlp.add_pipe("lemmatizer", config={"mode": "lookup"}) nlp.add_pipe("lemmatizer", config={"mode": "lookup"})
``` ```
### Rule-based lemmatizer {id="lemmatizer-rule"} ### Rule-based lemmatizer {id="lemmatizer-rule",model="morphologizer,tagger"}
When training pipelines that include a component that assigns part-of-speech When training pipelines that include a component that assigns part-of-speech
tags (a morphologizer or a tagger with a [POS mapping](#mappings-exceptions)), a tags (a morphologizer or a tagger with a [POS mapping](#mappings-exceptions)), a
@ -194,7 +194,7 @@ information, without consulting the context of the token. The rule-based
lemmatizer also accepts list-based exception files. For English, these are lemmatizer also accepts list-based exception files. For English, these are
acquired from [WordNet](https://wordnet.princeton.edu/). acquired from [WordNet](https://wordnet.princeton.edu/).
### Trainable lemmatizer {id="lemmatizer-train",model="lemmatizer"} ### Trainable lemmatizer {id="lemmatizer-train",model="trainable_lemmatizer"}
The [`EditTreeLemmatizer`](/api/edittreelemmatizer) can learn form-to-lemma The [`EditTreeLemmatizer`](/api/edittreelemmatizer) can learn form-to-lemma
transformations from a training corpus that includes lemma annotations. This transformations from a training corpus that includes lemma annotations. This