spaCy/website/docs/usage/101/_pipelines.md
2019-03-12 16:18:34 +01:00

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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 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.

The processing pipeline

  • 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 Doc Segment text into tokens.
tagger Tagger Doc[i].tag Assign part-of-speech tags.
parser DependencyParser Doc[i].head, Doc[i].dep, Doc.sents, Doc.noun_chunks Assign dependency labels.
ner EntityRecognizer Doc.ents, Doc[i].ent_iob, Doc[i].ent_type Detect and label named entities.
textcat TextCategorizer Doc.cats Assign document labels.
... 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:

"pipeline": ["tagger", "parser", "ner"]

import Accordion from 'components/accordion.js'

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 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.