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* document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
74 lines
4.6 KiB
Markdown
74 lines
4.6 KiB
Markdown
When you call `nlp` on a text, spaCy first tokenizes the text to produce a `Doc`
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object. The `Doc` is then processed in several different steps – this is also
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referred to as the **processing pipeline**. The pipeline used by the
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[default models](/models) consists of a tagger, a parser and an entity
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recognizer. Each pipeline component returns the processed `Doc`, which is then
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passed on to the next component.
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> - **Name**: ID of the pipeline component.
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> - **Component:** spaCy's implementation of the component.
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> - **Creates:** Objects, attributes and properties modified and set by the
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> component.
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| Name | Component | Creates | Description |
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| ----------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
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| **tokenizer** | [`Tokenizer`](/api/tokenizer) | `Doc` | Segment text into tokens. |
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| **tagger** | [`Tagger`](/api/tagger) | `Doc[i].tag` | Assign part-of-speech tags. |
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| **parser** | [`DependencyParser`](/api/dependencyparser) | `Doc[i].head`, `Doc[i].dep`, `Doc.sents`, `Doc.noun_chunks` | Assign dependency labels. |
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| **ner** | [`EntityRecognizer`](/api/entityrecognizer) | `Doc.ents`, `Doc[i].ent_iob`, `Doc[i].ent_type` | Detect and label named entities. |
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| **textcat** | [`TextCategorizer`](/api/textcategorizer) | `Doc.cats` | Assign document labels. |
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| ... | [custom components](/usage/processing-pipelines#custom-components) | `Doc._.xxx`, `Token._.xxx`, `Span._.xxx` | Assign custom attributes, methods or properties. |
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The processing pipeline always **depends on the statistical model** and its
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capabilities. For example, a pipeline can only include an entity recognizer
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component if the model includes data to make predictions of entity labels. This
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is why each model will specify the pipeline to use in its meta data, as a simple
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list containing the component names:
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```json
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"pipeline": ["tagger", "parser", "ner"]
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```
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import Accordion from 'components/accordion.js'
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<Accordion title="Does the order of pipeline components matter?" id="pipeline-components-order">
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In spaCy v2.x, the statistical components like the tagger or parser are
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independent and don't share any data between themselves. For example, the named
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entity recognizer doesn't use any features set by the tagger and parser, and so
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on. This means that you can swap them, or remove single components from the
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pipeline without affecting the others.
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However, custom components may depend on annotations set by other components.
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For example, a custom lemmatizer may need the part-of-speech tags assigned, so
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it'll only work if it's added after the tagger. The parser will respect
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pre-defined sentence boundaries, so if a previous component in the pipeline sets
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them, its dependency predictions may be different. Similarly, it matters if you
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add the [`EntityRuler`](/api/entityruler) before or after the statistical entity
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recognizer: if it's added before, the entity recognizer will take the existing
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entities into account when making predictions.
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The [`EntityLinker`](/api/entitylinker), which resolves named entities to
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knowledge base IDs, should be preceded by
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a pipeline component that recognizes entities such as the
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[`EntityRecognizer`](/api/entityrecognizer).
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</Accordion>
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<Accordion title="Why is the tokenizer special?" id="pipeline-components-tokenizer">
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The tokenizer is a "special" component and isn't part of the regular pipeline.
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It also doesn't show up in `nlp.pipe_names`. The reason is that there can only
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really be one tokenizer, and while all other pipeline components take a `Doc`
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and return it, the tokenizer takes a **string of text** and turns it into a
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`Doc`. You can still customize the tokenizer, though. `nlp.tokenizer` is
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writable, so you can either create your own
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[`Tokenizer` class from scratch](/usage/linguistic-features#native-tokenizers),
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or even replace it with an
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[entirely custom function](/usage/linguistic-features#custom-tokenizer).
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</Accordion>
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---
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