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Add Morphology and MorphAnalsysis API docs
Add initial draft of `Morphology` and `MorphAnalysis` API docs.
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Adriane Boyd
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website/docs/api/morphanalysis.md
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website/docs/api/morphanalysis.md
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---
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title: MorphAnalysis
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tag: class
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source: spacy/tokens/morphanalysis.pyx
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---
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Stores a single morphological analysis.
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## MorphAnalysis.\_\_init\_\_ {#init tag="method"}
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Initialize a MorphAnalysis object from a UD FEATS string or a dictionary of
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morphological features.
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> #### Example
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>
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> ```python
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> from spacy.tokens import MorphAnalysis
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>
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> feats = "Feat1=Val1|Feat2=Val2"
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> m = MorphAnalysis(nlp.vocab, feats)
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> ```
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| Name | Type | Description |
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| ----------- | ------------------ | ----------------------------- |
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| `vocab` | `Vocab` | The vocab. |
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| `features` | `Union[Dict, str]` | The morphological features. |
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| **RETURNS** | `MorphAnalysis` | The newly constructed object. |
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## MorphAnalysis.\_\_contains\_\_ {#contains tag="method"}
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Whether a feature/value pair is in the analysis.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1,Val2|Feat2=Val2"
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> morph = MorphAnalysis(nlp.vocab, feats)
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> assert "Feat1=Val1" in morph
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------------------- |
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| **RETURNS** | `str` | A feature/value pair in the analysis. |
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## MorphAnalysis.\_\_iter\_\_ {#iter tag="method"}
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Iterate over the feature/value pairs in the 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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> morph = MorphAnalysis(nlp.vocab, feats)
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> for feat in morph:
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> print(feat)
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> ```
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| Name | Type | Description |
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| ---------- | ----- | ------------------------------------- |
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| **YIELDS** | `str` | A feature/value pair in the analysis. |
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## MorphAnalysis.\_\_len\_\_ {#len tag="method"}
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Returns the number of features in the analysis.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1,Val2|Feat2=Val2"
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> morph = MorphAnalysis(nlp.vocab, feats)
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> assert len(morph) == 3
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> ```
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| Name | Type | Description |
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| ----------- | ----- | --------------------------------------- |
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| **RETURNS** | `int` | The number of features in the analysis. |
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## MorphAnalysis.\_\_str\_\_ {#str tag="method"}
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Returns the morphological analysis in the UD FEATS string format.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1,Val2|Feat2=Val2"
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> morph = MorphAnalysis(nlp.vocab, feats)
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> assert str(morph) == feats
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ---------------------------------|
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| **RETURNS** | `str` | The analysis in UD FEATS format. |
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## MorphAnalysis.get {#get tag="method"}
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Retrieve a feature by field.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1,Val2"
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> morph = MorphAnalysis(nlp.vocab, feats)
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> assert morph.get("Feat1") == ['Feat1=Val1', 'Feat1=Val2']
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> ```
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| Name | Type | Description |
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| ----------- | ------ | ----------------------------------- |
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| `field` | `str` | The field to retrieve. |
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| **RETURNS** | `list` | A list of the individual features. |
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## MorphAnalysis.to_dict {#to_dict tag="method"}
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Produce a dict representation of the analysis, in the same format as the tag
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map.
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> #### Example
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>
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> ```python
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> feats = "Feat1=Val1,Val2|Feat2=Val2"
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> morph = MorphAnalysis(nlp.vocab, feats)
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> assert morph.to_dict() == {'Feat1': 'Val1,Val2', 'Feat2': 'Val2'}
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> ```
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| Name | Type | Description |
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| ----------- | ------ | -----------------------------------------|
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| **RETURNS** | `dict` | The dict representation of the analysis. |
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## MorphAnalysis.from_id {#from_id tag="classmethod"}
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Create a morphological analysis from a given hash ID.
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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.strings[feats]
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> morph = MorphAnalysis.from_id(nlp.vocab, hash)
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> assert str(morph) == feats
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> ```
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| Name | Type | Description |
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| ------- | ------- | -------------------------------- |
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| `vocab` | `Vocab` | The vocab. |
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| `key` | `int` | The hash of the features string. |
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website/docs/api/morphology.md
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website/docs/api/morphology.md
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---
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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
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by 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 using the tag map, lemmatizer and exceptions.
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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, tag_map, lemmatizer)
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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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| `tag_map` | `Dict[str, Dict]` | The tag map. |
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| `lemmatizer`| `Lemmatizer` | The lemmatizer. |
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| `exc` | `Dict[str, Dict]` | A dictionary of exceptions in the format `{tag: {orth: {"POS": "X", "Feat1": "Val1, "Feat2": "Val2", ...}` |
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| **RETURNS** | `Morphology` | The newly constructed object. |
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## Morphology.add {#add tag="method"}
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Insert a morphological analysis in the morphology table, if not already
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present. The morphological analysis may be provided in the UD FEATS format as a
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string or in the tag map dictionary format. Returns the hash of the new
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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.load_tag_map {#load_tag_map tag="method"}
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Replace the current tag map with the provided tag map.
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| Name | Type | Description |
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| ----------- | ------------------ | ------------ |
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| `tag_map` | `Dict[str, Dict]` | The tag map. |
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## Morphology.load_morph_exceptions {#load_morph_exceptions tag="method"}
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Replace the current morphological exceptions with the provided exceptions.
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| Name | Type | Description |
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| ------------- | ------------------ | ----------------------------- |
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| `morph_rules` | `Dict[str, Dict]` | The morphological exceptions. |
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## Morphology.add_special_case {#add_special_case tag="method"}
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Add a special-case rule to the morphological analyzer. Tokens whose tag and
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orth match the rule will receive the specified properties.
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> #### Example
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>
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> ```python
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> attrs = {"POS": "DET", "Definite": "Def"}
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> morphology.add_special_case("DT", "the", attrs)
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ---------------------------------------------- |
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| `tag_str` | str | The fine-grained tag. |
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| `orth_str` | str | The token text. |
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| `attrs` | dict | The features to assign for this token and tag. |
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## Morphology.exc {#exc tag="property"}
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The current morphological exceptions.
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| Name | Type | Description |
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| ---------- | ----- | --------------------------------------------------- |
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| **YIELDS** | dict | The current dictionary of morphological exceptions. |
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## Morphology.lemmatize {#lemmatize tag="method"}
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TODO
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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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{ "text": "Token", "url": "/api/token" },
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{ "text": "Span", "url": "/api/span" },
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{ "text": "Lexeme", "url": "/api/lexeme" },
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{ "text": "MorphAnalysis", "url": "/api/morphanalysis" },
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{ "text": "Example", "url": "/api/example" },
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{ "text": "DocBin", "url": "/api/docbin" }
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]
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{ "text": "StringStore", "url": "/api/stringstore" },
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{ "text": "Vectors", "url": "/api/vectors" },
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{ "text": "Lookups", "url": "/api/lookups" },
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{ "text": "Morphology", "url": "/api/morphology" },
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{ "text": "KnowledgeBase", "url": "/api/kb" },
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{ "text": "Scorer", "url": "/api/scorer" },
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{ "text": "Corpus", "url": "/api/corpus" }
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