spaCy/lemmatizer.md at e829d3bf14fa2f6b15ef2a0ef4fa0fa7a37b49ad

mirror of https://github.com/explosion/spaCy.git synced 2025-02-28 01:20:38 +03:00

Add Lemmatizer and simplify related components (#5848 )

* 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* Remove outdated UPOS conversions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

2020-08-07 15:27:13 +02:00

14 KiB

Raw Blame History

title	tag	source	new	teaser	api_base_class	api_string_name	api_trainable
Lemmatizer	class	spacy/pipeline/lemmatizer.py	3	Pipeline component for lemmatization	/api/pipe	lemmatizer	false

Config and implementation

The default config is defined by the pipeline component factory and describes how the component should be configured. You can override its settings via the config argument on nlp.add_pipe or in your config.cfg for training.

For examples of the lookups data formats used by the lookup and rule-based lemmatizers, see the spacy-lookups-data repo.

Example

config = {"mode": "rule"}
nlp.add_pipe("lemmatizer", config=config)

Setting	Type	Description	Default
`mode`	str	The lemmatizer mode, e.g. "lookup" or "rule".	`"lookup"`
`lookups`	`Lookups`	The lookups object containing the tables such as "lemma_rules", "lemma_index", "lemma_exc" and "lemma_lookup". If `None`, default tables are loaded from `spacy-lookups-data`.	`None`
`overwrite`	bool	Whether to overwrite existing lemmas.	`False`
`model`	`Model`	Not yet implemented: the model to use.	`None`

https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/lemmatizer.py

Lemmatizer.init

Example

# Construction via add_pipe with default model
lemmatizer = nlp.add_pipe("lemmatizer")

# Construction via add_pipe with custom settings
config = {"mode": "rule", overwrite=True}
lemmatizer = nlp.add_pipe("lemmatizer", config=config)

Create a new pipeline instance. In your application, you would normally use a shortcut for this and instantiate the component using its string name and nlp.add_pipe.

Name	Type	Description
`vocab`	`Vocab`	The vocab.
`model`	`Model`	A model (not yet implemented).
`name`	str	String name of the component instance. Used to add entries to the `losses` during training.
keyword-only
mode	str	The lemmatizer mode, e.g. "lookup" or "rule". Defaults to "lookup".
lookups	`Lookups`	A lookups object containing the tables such as "lemma_rules", "lemma_index", "lemma_exc" and "lemma_lookup". Defaults to `None`.
overwrite	bool	Whether to overwrite existing lemmas.

Lemmatizer.call

Apply the pipe to one document. The document is modified in place, and returned. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order.

Example

doc = nlp("This is a sentence.")
lemmatizer = nlp.add_pipe("lemmatizer")
# This usually happens under the hood
processed = lemmatizer(doc)

Name	Type	Description
`doc`	`Doc`	The document to process.
RETURNS	`Doc`	The processed document.

Lemmatizer.pipe

Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all pipeline components are applied to the Doc in order.

Example

lemmatizer = nlp.add_pipe("lemmatizer")
for doc in lemmatizer.pipe(docs, batch_size=50):
    pass

Name	Type	Description
`stream`	`Iterable[Doc]`	A stream of documents.
keyword-only
`batch_size`	int	The number of texts to buffer. Defaults to `128`.
YIELDS	`Doc`	Processed documents in the order of the original text.

Lemmatizer.lookup_lemmatize

Lemmatize a token using a lookup-based approach. If no lemma is found, the original string is returned. Languages can provide a lookup table via the Lookups.

Name	Type	Description
`token`	`Token`	The token to lemmatize.
RETURNS	`List[str]`	A list containing one or more lemmas.

Lemmatizer.rule_lemmatize

Lemmatize a token using a rule-based approach. Typically relies on POS tags.

Name	Type	Description
`token`	`Token`	The token to lemmatize.
RETURNS	`List[str]`	A list containing one or more lemmas.

Lemmatizer.is_base_form

Check whether we're dealing with an uninflected paradigm, so we can avoid lemmatization entirely.

Name	Type	Description
`token`	`Token`	The token to analyze.
RETURNS	bool	Whether the token's attributes (e.g., part-of-speech tag, morphological features) describe a base form.

Lemmatizer.get_lookups_config

Returns the lookups configuration settings for a given mode for use in Lemmatizer.load_lookups.

Name	Type	Description
`mode`	str	The lemmatizer mode.
RETURNS	dict	The lookups configuration settings for this mode.

Lemmatizer.load_lookups

Load and validate lookups tables. If the provided lookups is None, load the default lookups tables according to the language and mode settings. Confirm that all required tables for the language and mode are present.

Name	Type	Description
`lang`	str	The language.
`mode`	str	The lemmatizer mode.
`lookups`	`Lookups`	The provided lookups, may be `None` if the default lookups should be loaded.
RETURNS	`Lookups`	The lookups object.

Lemmatizer.to_disk

Serialize the pipe to disk.

Example

lemmatizer = nlp.add_pipe("lemmatizer")
lemmatizer.to_disk("/path/to/lemmatizer")

Name	Type	Description
`path`	str / `Path`	A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects.
keyword-only
`exclude`	`Iterable[str]`	String names of serialization fields to exclude.

Lemmatizer.from_disk

Load the pipe from disk. Modifies the object in place and returns it.

Example

lemmatizer = nlp.add_pipe("lemmatizer")
lemmatizer.from_disk("/path/to/lemmatizer")

Name	Type	Description
`path`	str / `Path`	A path to a directory. Paths may be either strings or `Path`-like objects.
keyword-only
`exclude`	`Iterable[str]`	String names of serialization fields to exclude.
RETURNS	`Lemmatizer`	The modified `Lemmatizer` object.

Lemmatizer.to_bytes

Example

lemmatizer = nlp.add_pipe("lemmatizer")
lemmatizer_bytes = lemmatizer.to_bytes()

Serialize the pipe to a bytestring.

Name	Type	Description
keyword-only
`exclude`	`Iterable[str]`	String names of serialization fields to exclude.
RETURNS	bytes	The serialized form of the `Lemmatizer` object.

Lemmatizer.from_bytes

Load the pipe from a bytestring. Modifies the object in place and returns it.

Example

lemmatizer_bytes = lemmatizer.to_bytes()
lemmatizer = nlp.add_pipe("lemmatizer")
lemmatizer.from_bytes(lemmatizer_bytes)

Name	Type	Description
`bytes_data`	bytes	The data to load from.
keyword-only
`exclude`	`Iterable[str]`	String names of serialization fields to exclude.
RETURNS	`Lemmatizer`	The `Lemmatizer` object.

Lemmatizer.mode

The lemmatizer mode.

Name	Type	Description
RETURNS	`str`	The lemmatizer mode.

Attributes

Name	Type	Description
`vocab`	The shared `Vocab`.
`lookups`	`Lookups`	The lookups object.

Serialization fields

During serialization, spaCy will export several data fields used to restore different aspects of the object. If needed, you can exclude them from serialization by passing in the string names via the exclude argument.

Example

data = lemmatizer.to_disk("/path", exclude=["vocab"])

Name	Description
`vocab`	The shared `Vocab`.
`lookups`	The lookups. You usually don't want to exclude this.

14 KiB Raw Blame History

Config and implementation

Example

Lemmatizer.__init__

Example

Lemmatizer.__call__

Example

Lemmatizer.pipe

Example

Lemmatizer.lookup_lemmatize

Lemmatizer.rule_lemmatize

Lemmatizer.is_base_form

Lemmatizer.get_lookups_config

Lemmatizer.load_lookups

Lemmatizer.to_disk

Example

Lemmatizer.from_disk

Example

Lemmatizer.to_bytes

Example

Lemmatizer.from_bytes

Example

Lemmatizer.mode

Attributes

Serialization fields

Example

14 KiB

Raw Blame History

Lemmatizer.init

Lemmatizer.call