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* NEL: read sentences and ents from reference * fiddling with sent_start annotations * add KB serialization test * KB write additional file with strings.json * score_links function to calculate NEL P/R/F * formatting * documentation
328 lines
18 KiB
Markdown
328 lines
18 KiB
Markdown
---
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title: EntityLinker
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tag: class
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source: spacy/pipeline/entity_linker.py
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new: 2.2
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teaser: 'Pipeline component for named entity linking and disambiguation'
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api_base_class: /api/pipe
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api_string_name: entity_linker
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api_trainable: true
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---
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An `EntityLinker` component disambiguates textual mentions (tagged as named
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entities) to unique identifiers, grounding the named entities into the "real
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world". It requires a `KnowledgeBase`, as well as a function to generate
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plausible candidates from that `KnowledgeBase` given a certain textual mention,
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and a machine learning model to pick the right candidate, given the local
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context of the mention.
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config). See the
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[model architectures](/api/architectures) documentation for details on the
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architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy.pipeline.entity_linker import DEFAULT_NEL_MODEL
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> config = {
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> "labels_discard": [],
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> "incl_prior": True,
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> "incl_context": True,
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> "model": DEFAULT_NEL_MODEL,
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> "kb_loader": {'@misc': 'spacy.EmptyKB.v1', 'entity_vector_length': 64},
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> "get_candidates": {'@misc': 'spacy.CandidateGenerator.v1'},
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> }
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> nlp.add_pipe("entity_linker", config=config)
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> ```
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| Setting | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `labels_discard` | NER labels that will automatically get a "NIL" prediction. Defaults to `[]`. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. Defaults to `True`. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. Defaults to `True`. ~~bool~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [EntityLinker](/api/architectures#EntityLinker). ~~Model~~ |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. Defaults to [EmptyKB](/api/architectures#EmptyKB), a function returning an empty `KnowledgeBase` with an `entity_vector_length` of `64`. ~~Callable[[Vocab], KnowledgeBase]~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. Defaults to [CandidateGenerator](/api/architectures#CandidateGenerator), a function looking up exact, case-dependent aliases in the KB. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
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```
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## EntityLinker.\_\_init\_\_ {#init tag="method"}
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> entity_linker = nlp.add_pipe("entity_linker")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_el.v1"}}
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> entity_linker = nlp.add_pipe("entity_linker", config=config)
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>
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> # Construction via add_pipe with custom KB and candidate generation
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> config = {"kb": {"@misc": "my_kb.v1"}}
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> entity_linker = nlp.add_pipe("entity_linker", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import EntityLinker
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> entity_linker = EntityLinker(nlp.vocab, model)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#add_pipe). Note that both the internal
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`KnowledgeBase` as well as the Candidate generator can be customized by
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providing custom registered functions.
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| Name | Description |
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| ---------------- | -------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
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| `get_candidates` | Function that generates plausible candidates for a given `Span` object. ~~Callable[[KnowledgeBase, Span], Iterable[Candidate]]~~ |
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| `labels_discard` | NER labels that will automatically get a `"NIL"` prediction. ~~Iterable[str]~~ |
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| `incl_prior` | Whether or not to include prior probabilities from the KB in the model. ~~bool~~ |
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| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
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## EntityLinker.\_\_call\_\_ {#call tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/entitylinker#call) and [`pipe`](/api/entitylinker#pipe)
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delegate to the [`predict`](/api/entitylinker#predict) and
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[`set_annotations`](/api/entitylinker#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> entity_linker = nlp.add_pipe("entity_linker")
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> # This usually happens under the hood
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> processed = entity_linker(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## EntityLinker.pipe {#pipe tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
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[`pipe`](/api/entitylinker#pipe) delegate to the
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[`predict`](/api/entitylinker#predict) and
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[`set_annotations`](/api/entitylinker#set_annotations) methods.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> for doc in entity_linker.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## EntityLinker.begin_training {#begin_training tag="method"}
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker", last=True)
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> optimizer = entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## EntityLinker.predict {#predict tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
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modifying them. Returns the KB IDs for each entity in each doc, including `NIL`
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if there is no prediction.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> kb_ids = entity_linker.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | `List[str]` | The predicted KB identifiers for the entities in the `docs`. ~~List[str]~~ |
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## EntityLinker.set_annotations {#set_annotations tag="method"}
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Modify a batch of documents, using pre-computed entity IDs for a list of named
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entities.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> kb_ids = entity_linker.predict([doc1, doc2])
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> entity_linker.set_annotations([doc1, doc2], kb_ids)
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> ```
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| Name | Description |
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| -------- | --------------------------------------------------------------------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `kb_ids` | The knowledge base identifiers for the entities in the docs, predicted by `EntityLinker.predict`. ~~List[str]~~ |
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## EntityLinker.update {#update tag="method"}
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Learn from a batch of [`Example`](/api/example) objects, updating both the
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pipe's entity linking model and context encoder. Delegates to
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[`predict`](/api/entitylinker#predict).
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> optimizer = nlp.begin_training()
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> losses = entity_linker.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `set_annotations` | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](#set_annotations). ~~bool~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## EntityLinker.score {#score tag="method" new="3"}
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Score a batch of examples.
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> #### Example
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>
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> ```python
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> scores = entity_linker.score(examples)
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------------------------------------------- |
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| `examples` | The examples to score. ~~Iterable[Example]~~ |
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| **RETURNS** | The scores, produced by [`Scorer.score_links`](/api/scorer#score_links) . ~~Dict[str, float]~~ |
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## EntityLinker.create_optimizer {#create_optimizer tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> optimizer = entity_linker.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## EntityLinker.use_params {#use_params tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> with entity_linker.use_params(optimizer.averages):
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> entity_linker.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## EntityLinker.to_disk {#to_disk tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> entity_linker.to_disk("/path/to/entity_linker")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## EntityLinker.from_disk {#from_disk tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker")
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> entity_linker.from_disk("/path/to/entity_linker")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `EntityLinker` object. ~~EntityLinker~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = entity_linker.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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| `kb` | The knowledge base. You usually don't want to exclude this. |
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