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103 lines
4.9 KiB
Markdown
103 lines
4.9 KiB
Markdown
---
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title: Lemmatizer
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teaser: Assign the base forms of words
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tag: class
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source: spacy/lemmatizer.py
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---
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<!-- TODO: rewrite once it's converted to pipe -->
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The `Lemmatizer` supports simple part-of-speech-sensitive suffix rules and
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lookup tables.
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## Lemmatizer.\_\_init\_\_ {#init tag="method"}
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Initialize a `Lemmatizer`. Typically, this happens under the hood within spaCy
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when a `Language` subclass and its `Vocab` is initialized.
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> #### Example
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>
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> ```python
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> from spacy.lemmatizer import Lemmatizer
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> from spacy.lookups import Lookups
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> lookups = Lookups()
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> lookups.add_table("lemma_rules", {"noun": [["s", ""]]})
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> lemmatizer = Lemmatizer(lookups)
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> ```
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>
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> For examples of the data format, see the
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> [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) repo.
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| Name | Type | Description |
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| -------------------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
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| `lookups` <Tag variant="new">2.2</Tag> | [`Lookups`](/api/lookups) | The lookups object containing the (optional) tables `"lemma_rules"`, `"lemma_index"`, `"lemma_exc"` and `"lemma_lookup"`. |
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## Lemmatizer.\_\_call\_\_ {#call tag="method"}
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Lemmatize a string.
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> #### Example
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>
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> ```python
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> from spacy.lemmatizer import Lemmatizer
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> from spacy.lookups import Lookups
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> lookups = Lookups()
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> lookups.add_table("lemma_rules", {"noun": [["s", ""]]})
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> lemmatizer = Lemmatizer(lookups)
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> lemmas = lemmatizer("ducks", "NOUN")
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> assert lemmas == ["duck"]
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> ```
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| Name | Type | Description |
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| ------------ | ------------- | -------------------------------------------------------------------------------------------------------- |
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| `string` | str | The string to lemmatize, e.g. the token text. |
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| `univ_pos` | str / int | The token's universal part-of-speech tag. |
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| `morphology` | dict / `None` | Morphological features following the [Universal Dependencies](http://universaldependencies.org/) scheme. |
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| **RETURNS** | list | The available lemmas for the string. |
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## Lemmatizer.lookup {#lookup tag="method" new="2"}
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Look up a lemma in the lookup table, if available. If no lemma is found, the
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original string is returned. Languages can provide a
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[lookup table](/usage/adding-languages#lemmatizer) via the `Lookups`.
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> #### Example
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>
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> ```python
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> lookups = Lookups()
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> lookups.add_table("lemma_lookup", {"going": "go"})
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> assert lemmatizer.lookup("going") == "go"
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ----------------------------------------------------------------------------------------------------------- |
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| `string` | str | The string to look up. |
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| `orth` | int | Optional hash of the string to look up. If not set, the string will be used and hashed. Defaults to `None`. |
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| **RETURNS** | str | The lemma if the string was found, otherwise the original string. |
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## Lemmatizer.is_base_form {#is_base_form tag="method"}
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Check whether we're dealing with an uninflected paradigm, so we can avoid
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lemmatization entirely.
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> #### Example
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>
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> ```python
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> pos = "verb"
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> morph = {"VerbForm": "inf"}
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> is_base_form = lemmatizer.is_base_form(pos, morph)
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> assert is_base_form == True
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> ```
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| Name | Type | Description |
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| ------------ | --------- | --------------------------------------------------------------------------------------- |
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| `univ_pos` | str / int | The token's universal part-of-speech tag. |
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| `morphology` | dict | The token's morphological features. |
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| **RETURNS** | bool | Whether the token's part-of-speech tag and morphological features describe a base form. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| -------------------------------------- | ------------------------- | --------------------------------------------------------------- |
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| `lookups` <Tag variant="new">2.2</Tag> | [`Lookups`](/api/lookups) | The lookups object containing the rules and data, if available. |
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