mirror of https://github.com/explosion/spaCy.git
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`
|
||
object. The `Doc` is then processed in several different steps – this is also
|
||
referred to as the **processing pipeline**. The pipeline used by the
|
||
[default models](/models) consists of a tagger, a parser and an entity
|
||
recognizer. Each pipeline component returns the processed `Doc`, which is then
|
||
passed on to the next component.
|
||
|
||
|
||
|
||
> - **Name**: ID of the pipeline component.
|
||
> - **Component:** spaCy's implementation of the component.
|
||
> - **Creates:** Objects, attributes and properties modified and set by the
|
||
> component.
|
||
|
||
| Name | Component | Creates | Description |
|
||
| ----------------- | ------------------------------------------------------------------ | ----------------------------------------------------------- | ------------------------------------------------ |
|
||
| **tokenizer** | [`Tokenizer`](/api/tokenizer) | `Doc` | Segment text into tokens. |
|
||
| **tagger** | [`Tagger`](/api/tagger) | `Doc[i].tag` | Assign part-of-speech tags. |
|
||
| **parser** | [`DependencyParser`](/api/dependencyparser) | `Doc[i].head`, `Doc[i].dep`, `Doc.sents`, `Doc.noun_chunks` | Assign dependency labels. |
|
||
| **ner** | [`EntityRecognizer`](/api/entityrecognizer) | `Doc.ents`, `Doc[i].ent_iob`, `Doc[i].ent_type` | Detect and label named entities. |
|
||
| **textcat** | [`TextCategorizer`](/api/textcategorizer) | `Doc.cats` | Assign document labels. |
|
||
| ... | [custom components](/usage/processing-pipelines#custom-components) | `Doc._.xxx`, `Token._.xxx`, `Span._.xxx` | Assign custom attributes, methods or properties. |
|
||
|
||
The processing pipeline always **depends on the statistical model** and its
|
||
capabilities. For example, a pipeline can only include an entity recognizer
|
||
component if the model includes data to make predictions of entity labels. This
|
||
is why each model will specify the pipeline to use in its meta data, as a simple
|
||
list containing the component names:
|
||
|
||
```json
|
||
"pipeline": ["tagger", "parser", "ner"]
|
||
```
|
||
|
||
import Accordion from 'components/accordion.js'
|
||
|
||
<Accordion title="Does the order of pipeline components matter?" id="pipeline-components-order">
|
||
|
||
In spaCy v2.x, the statistical components like the tagger or parser are
|
||
independent and don't share any data between themselves. For example, the named
|
||
entity recognizer doesn't use any features set by the tagger and parser, and so
|
||
on. This means that you can swap them, or remove single components from the
|
||
pipeline without affecting the others.
|
||
|
||
However, custom components may depend on annotations set by other components.
|
||
For example, a custom lemmatizer may need the part-of-speech tags assigned, so
|
||
it'll only work if it's added after the tagger. The parser will respect
|
||
pre-defined sentence boundaries, so if a previous component in the pipeline sets
|
||
them, its dependency predictions may be different. Similarly, it matters if you
|
||
add the [`EntityRuler`](/api/entityruler) before or after the statistical entity
|
||
recognizer: if it's added before, the entity recognizer will take the existing
|
||
entities into account when making predictions.
|
||
The [`EntityLinker`](/api/entitylinker), which resolves named entities to
|
||
knowledge base IDs, should be preceded by
|
||
a pipeline component that recognizes entities such as the
|
||
[`EntityRecognizer`](/api/entityrecognizer).
|
||
|
||
</Accordion>
|
||
|
||
<Accordion title="Why is the tokenizer special?" id="pipeline-components-tokenizer">
|
||
|
||
The tokenizer is a "special" component and isn't part of the regular pipeline.
|
||
It also doesn't show up in `nlp.pipe_names`. The reason is that there can only
|
||
really be one tokenizer, and while all other pipeline components take a `Doc`
|
||
and return it, the tokenizer takes a **string of text** and turns it into a
|
||
`Doc`. You can still customize the tokenizer, though. `nlp.tokenizer` is
|
||
writable, so you can either create your own
|
||
[`Tokenizer` class from scratch](/usage/linguistic-features#native-tokenizers),
|
||
or even replace it with an
|
||
[entirely custom function](/usage/linguistic-features#custom-tokenizer).
|
||
|
||
</Accordion>
|
||
|
||
---
|