mirror of https://github.com/explosion/spaCy.git
329 lines
16 KiB
Plaintext
329 lines
16 KiB
Plaintext
---
|
|
title: Lemmatizer
|
|
tag: class
|
|
source: spacy/pipeline/lemmatizer.py
|
|
version: 3
|
|
teaser: 'Pipeline component for lemmatization'
|
|
api_string_name: lemmatizer
|
|
api_trainable: false
|
|
---
|
|
|
|
Component for assigning base forms to tokens using rules based on part-of-speech
|
|
tags, or lookup tables. Different [`Language`](/api/language) subclasses can
|
|
implement their own lemmatizer components via
|
|
[language-specific factories](/usage/processing-pipelines#factories-language).
|
|
The default data used is provided by the
|
|
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
|
|
extension package.
|
|
|
|
For a trainable lemmatizer, see [`EditTreeLemmatizer`](/api/edittreelemmatizer).
|
|
|
|
<Infobox variant="warning" title="New in v3.0">
|
|
|
|
As of v3.0, the `Lemmatizer` is a **standalone pipeline component** that can be
|
|
added to your pipeline, and not a hidden part of the vocab that runs behind the
|
|
scenes. This makes it easier to customize how lemmas should be assigned in your
|
|
pipeline.
|
|
|
|
If the lemmatization mode is set to `"rule"`, which requires coarse-grained POS
|
|
(`Token.pos`) to be assigned, make sure a [`Tagger`](/api/tagger),
|
|
[`Morphologizer`](/api/morphologizer) or another component assigning POS is
|
|
available in the pipeline and runs _before_ the lemmatizer.
|
|
|
|
</Infobox>
|
|
|
|
## Assigned Attributes {id="assigned-attributes"}
|
|
|
|
Lemmas generated by rules or predicted will be saved to `Token.lemma`.
|
|
|
|
| Location | Value |
|
|
| -------------- | ------------------------- |
|
|
| `Token.lemma` | The lemma (hash). ~~int~~ |
|
|
| `Token.lemma_` | The lemma. ~~str~~ |
|
|
|
|
## 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`](/api/language#add_pipe) or in your
|
|
[`config.cfg` for training](/usage/training#config). For examples of the lookups
|
|
data format used by the lookup and rule-based lemmatizers, see
|
|
[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> config = {"mode": "rule"}
|
|
> nlp.add_pipe("lemmatizer", config=config)
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| -------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `mode` | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `lookup` if no language-specific lemmatizer is available (see the following table). ~~str~~ |
|
|
| `overwrite` | Whether to overwrite existing lemmas. Defaults to `False`. ~~bool~~ |
|
|
| `model` | **Not yet implemented:** the model to use. ~~Model~~ |
|
|
| _keyword-only_ | |
|
|
| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"lemma"`. ~~Optional[Callable]~~ |
|
|
|
|
Many languages specify a default lemmatizer mode other than `lookup` if a better
|
|
lemmatizer is available. The lemmatizer modes `rule` and `pos_lookup` require
|
|
[`token.pos`](/api/token) from a previous pipeline component (see example
|
|
pipeline configurations in the
|
|
[pretrained pipeline design details](/models#design-cnn)) or rely on third-party
|
|
libraries (`pymorphy3`).
|
|
|
|
| Language | Default Mode |
|
|
| -------- | ------------ |
|
|
| `bn` | `rule` |
|
|
| `ca` | `pos_lookup` |
|
|
| `el` | `rule` |
|
|
| `en` | `rule` |
|
|
| `es` | `rule` |
|
|
| `fa` | `rule` |
|
|
| `fr` | `rule` |
|
|
| `it` | `pos_lookup` |
|
|
| `mk` | `rule` |
|
|
| `nb` | `rule` |
|
|
| `nl` | `rule` |
|
|
| `pl` | `pos_lookup` |
|
|
| `ru` | `pymorphy3` |
|
|
| `sv` | `rule` |
|
|
| `uk` | `pymorphy3` |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/lemmatizer.py
|
|
```
|
|
|
|
## Lemmatizer.\_\_init\_\_ {id="init",tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # 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`](/api/language#add_pipe).
|
|
|
|
| Name | Description |
|
|
| -------------- | --------------------------------------------------------------------------------------------------- |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `model` | **Not yet implemented:** The model to use. ~~Model~~ |
|
|
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| mode | The lemmatizer mode, e.g. `"lookup"` or `"rule"`. Defaults to `"lookup"`. ~~str~~ |
|
|
| overwrite | Whether to overwrite existing lemmas. ~~bool~~ |
|
|
|
|
## Lemmatizer.\_\_call\_\_ {id="call",tag="method"}
|
|
|
|
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
|
|
>
|
|
> ```python
|
|
> doc = nlp("This is a sentence.")
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> # This usually happens under the hood
|
|
> processed = lemmatizer(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## Lemmatizer.pipe {id="pipe",tag="method"}
|
|
|
|
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
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> for doc in lemmatizer.pipe(docs, batch_size=50):
|
|
> pass
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------- |
|
|
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
|
|
| _keyword-only_ | |
|
|
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
|
|
| **YIELDS** | The processed documents in order. ~~Doc~~ |
|
|
|
|
## Lemmatizer.initialize {id="initialize",tag="method"}
|
|
|
|
Initialize the lemmatizer and load any data resources. This method is typically
|
|
called by [`Language.initialize`](/api/language#initialize) and lets you
|
|
customize arguments it receives via the
|
|
[`[initialize.components]`](/api/data-formats#config-initialize) block in the
|
|
config. The loading only happens during initialization, typically before
|
|
training. At runtime, all data is loaded from disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> lemmatizer.initialize(lookups=lookups)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.lemmatizer]
|
|
>
|
|
> [initialize.components.lemmatizer.lookups]
|
|
> @misc = "load_my_lookups.v1"
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Defaults to `None`. ~~Optional[Callable[[], Iterable[Example]]]~~ |
|
|
| _keyword-only_ | |
|
|
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
|
| `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`](https://github.com/explosion/spacy-lookups-data). Defaults to `None`. ~~Optional[Lookups]~~ |
|
|
|
|
## Lemmatizer.lookup_lemmatize {id="lookup_lemmatize",tag="method"}
|
|
|
|
Lemmatize a token using a lookup-based approach. If no lemma is found, the
|
|
original string is returned.
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------- |
|
|
| `token` | The token to lemmatize. ~~Token~~ |
|
|
| **RETURNS** | A list containing one or more lemmas. ~~List[str]~~ |
|
|
|
|
## Lemmatizer.rule_lemmatize {id="rule_lemmatize",tag="method"}
|
|
|
|
Lemmatize a token using a rule-based approach. Typically relies on POS tags.
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------- |
|
|
| `token` | The token to lemmatize. ~~Token~~ |
|
|
| **RETURNS** | A list containing one or more lemmas. ~~List[str]~~ |
|
|
|
|
## Lemmatizer.is_base_form {id="is_base_form",tag="method"}
|
|
|
|
Check whether we're dealing with an uninflected paradigm, so we can avoid
|
|
lemmatization entirely.
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------------------------------------------------------------------------------------------- |
|
|
| `token` | The token to analyze. ~~Token~~ |
|
|
| **RETURNS** | Whether the token's attributes (e.g., part-of-speech tag, morphological features) describe a base form. ~~bool~~ |
|
|
|
|
## Lemmatizer.get_lookups_config {id="get_lookups_config",tag="classmethod"}
|
|
|
|
Returns the lookups configuration settings for a given mode for use in
|
|
[`Lemmatizer.load_lookups`](/api/lemmatizer#load_lookups).
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------------------------- |
|
|
| `mode` | The lemmatizer mode. ~~str~~ |
|
|
| **RETURNS** | The required table names and the optional table names. ~~Tuple[List[str], List[str]]~~ |
|
|
|
|
## Lemmatizer.to_disk {id="to_disk",tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> lemmatizer.to_disk("/path/to/lemmatizer")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
|
|
## Lemmatizer.from_disk {id="from_disk",tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> lemmatizer.from_disk("/path/to/lemmatizer")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ----------------------------------------------------------------------------------------------- |
|
|
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The modified `Lemmatizer` object. ~~Lemmatizer~~ |
|
|
|
|
## Lemmatizer.to_bytes {id="to_bytes",tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> lemmatizer_bytes = lemmatizer.to_bytes()
|
|
> ```
|
|
|
|
Serialize the pipe to a bytestring.
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------- |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The serialized form of the `Lemmatizer` object. ~~bytes~~ |
|
|
|
|
## Lemmatizer.from_bytes {id="from_bytes",tag="method"}
|
|
|
|
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer_bytes = lemmatizer.to_bytes()
|
|
> lemmatizer = nlp.add_pipe("lemmatizer")
|
|
> lemmatizer.from_bytes(lemmatizer_bytes)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------- |
|
|
| `bytes_data` | The data to load from. ~~bytes~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The `Lemmatizer` object. ~~Lemmatizer~~ |
|
|
|
|
## Attributes {id="attributes"}
|
|
|
|
| Name | Description |
|
|
| --------- | ------------------------------------------- |
|
|
| `vocab` | The shared [`Vocab`](/api/vocab). ~~Vocab~~ |
|
|
| `lookups` | The lookups object. ~~Lookups~~ |
|
|
| `mode` | The lemmatizer mode. ~~str~~ |
|
|
|
|
## Serialization fields {id="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
|
|
>
|
|
> ```python
|
|
> data = lemmatizer.to_disk("/path", exclude=["vocab"])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| --------- | ---------------------------------------------------- |
|
|
| `vocab` | The shared [`Vocab`](/api/vocab). |
|
|
| `lookups` | The lookups. You usually don't want to exclude this. |
|