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* Add Lemmatizer and simplify related components * Add `Lemmatizer` pipe with `lookup` and `rule` modes using the `Lookups` tables. * Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma) * Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer, or morph rules) * Remove lemmatizer from `Vocab` * Adjust many many tests Differences: * No default lookup lemmas * No special treatment of TAG in `from_array` and similar required * Easier to modify labels in a `Tagger` * No extra strings added from morphology / tag map * Fix test * Initial fix for Lemmatizer config/serialization * Adjust init test to be more generic * Adjust init test to force empty Lookups * Add simple cache to rule-based lemmatizer * Convert language-specific lemmatizers Convert language-specific lemmatizers to component lemmatizers. Remove previous lemmatizer class. * Fix French and Polish lemmatizers * Remove outdated UPOS conversions * Update Russian lemmatizer init in tests * Add minimal init/run tests for custom lemmatizers * Add option to overwrite existing lemmas * Update mode setting, lookup loading, and caching * Make `mode` an immutable property * Only enforce strict `load_lookups` for known supported modes * Move caching into individual `_lemmatize` methods * Implement strict when lang is not found in lookups * Fix tables/lookups in make_lemmatizer * Reallow provided lookups and allow for stricter checks * Add lookups asset to all Lemmatizer pipe tests * Rename lookups in lemmatizer init test * Clean up merge * Refactor lookup table loading * Add helper from `load_lemmatizer_lookups` that loads required and optional lookups tables based on settings provided by a config. Additional slight refactor of lookups: * Add `Lookups.set_table` to set a table from a provided `Table` * Reorder class definitions to be able to specify type as `Table` * Move registry assets into test methods * Refactor lookups tables config Use class methods within `Lemmatizer` to provide the config for particular modes and to load the lookups from a config. * Add pipe and score to lemmatizer * Simplify Tagger.score * Add missing import * Clean up imports and auto-format * Remove unused kwarg * Tidy up and auto-format * Update docstrings for Lemmatizer Update docstrings for Lemmatizer. Additionally modify `is_base_form` API to take `Token` instead of individual features. * Update docstrings * Remove tag map values from Tagger.add_label * Update API docs * Fix relative link in Lemmatizer API docs
104 lines
3.5 KiB
Markdown
104 lines
3.5 KiB
Markdown
---
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title: Morphology
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tag: class
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source: spacy/morphology.pyx
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---
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Store the possible morphological analyses for a language, and index them by
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hash. To save space on each token, tokens only know the hash of their
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morphological analysis, so queries of morphological attributes are delegated to
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this class.
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## Morphology.\_\_init\_\_ {#init tag="method"}
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Create a Morphology object.
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> #### Example
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>
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> ```python
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> from spacy.morphology import Morphology
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>
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> morphology = Morphology(strings)
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> ```
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| Name | Type | Description |
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| --------- | ------------- | ----------------- |
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| `strings` | `StringStore` | The string store. |
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## Morphology.add {#add tag="method"}
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Insert a morphological analysis in the morphology table, if not already present.
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The morphological analysis may be provided in the UD FEATS format as a string or
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in the tag map dictionary format. Returns the hash of the new analysis.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1|Feat2=Val2"
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> hash = nlp.vocab.morphology.add(feats)
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> assert hash == nlp.vocab.strings[feats]
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> ```
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| Name | Type | Description |
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| ---------- | ------------------ | --------------------------- |
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| `features` | `Union[Dict, str]` | The morphological features. |
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## Morphology.get {#get tag="method"}
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1|Feat2=Val2"
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> hash = nlp.vocab.morphology.add(feats)
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> assert nlp.vocab.morphology.get(hash) == feats
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> ```
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Get the FEATS string for the hash of the morphological analysis.
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| Name | Type | Description |
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| ------- | ---- | --------------------------------------- |
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| `morph` | int | The hash of the morphological analysis. |
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## Morphology.feats_to_dict {#feats_to_dict tag="staticmethod"}
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Convert a string FEATS representation to a dictionary of features and values in
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the same format as the tag map.
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> #### Example
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>
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> ```python
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> from spacy.morphology import Morphology
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> d = Morphology.feats_to_dict("Feat1=Val1|Feat2=Val2")
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> assert d == {"Feat1": "Val1", "Feat2": "Val2"}
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ------------------------------------------------------------------ |
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| `feats` | str | The morphological features in Universal Dependencies FEATS format. |
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| **RETURNS** | dict | The morphological features as a dictionary. |
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## Morphology.dict_to_feats {#dict_to_feats tag="staticmethod"}
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Convert a dictionary of features and values to a string FEATS representation.
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> #### Example
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>
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> ```python
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> from spacy.morphology import Morphology
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> f = Morphology.dict_to_feats({"Feat1": "Val1", "Feat2": "Val2"})
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> assert f == "Feat1=Val1|Feat2=Val2"
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> ```
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| Name | Type | Description |
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| ------------ | ----------------- | --------------------------------------------------------------------- |
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| `feats_dict` | `Dict[str, Dict]` | The morphological features as a dictionary. |
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| **RETURNS** | str | The morphological features as in Universal Dependencies FEATS format. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| ------------- | ----- | -------------------------------------------- |
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| `FEATURE_SEP` | `str` | The FEATS feature separator. Default is `|`. |
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| `FIELD_SEP` | `str` | The FEATS field separator. Default is `=`. |
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| `VALUE_SEP` | `str` | The FEATS value separator. Default is `,`. |
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