spaCy/website/docs/api/pipeline-functions.md
2020-08-17 16:45:24 +02:00

110 lines
4.0 KiB
Markdown

---
title: Pipeline Functions
teaser: Other built-in pipeline components and helpers
source: spacy/pipeline/functions.py
menu:
- ['merge_noun_chunks', 'merge_noun_chunks']
- ['merge_entities', 'merge_entities']
- ['merge_subtokens', 'merge_subtokens']
---
## merge_noun_chunks {#merge_noun_chunks tag="function"}
Merge noun chunks into a single token. Also available via the string name
`"merge_noun_chunks"`.
> #### Example
>
> ```python
> texts = [t.text for t in nlp("I have a blue car")]
> assert texts == ["I", "have", "a", "blue", "car"]
>
> nlp.add_pipe("merge_noun_chunks")
> texts = [t.text for t in nlp("I have a blue car")]
> assert texts == ["I", "have", "a blue car"]
> ```
<Infobox variant="warning">
Since noun chunks require part-of-speech tags and the dependency parse, make
sure to add this component _after_ the `"tagger"` and `"parser"` components. By
default, `nlp.add_pipe` will add components to the end of the pipeline and after
all other components.
</Infobox>
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with merged noun chunks. ~~Doc~~ |
## merge_entities {#merge_entities tag="function"}
Merge named entities into a single token. Also available via the string name
`"merge_entities"`.
> #### Example
>
> ```python
> texts = [t.text for t in nlp("I like David Bowie")]
> assert texts == ["I", "like", "David", "Bowie"]
>
> nlp.add_pipe("merge_entities")
>
> texts = [t.text for t in nlp("I like David Bowie")]
> assert texts == ["I", "like", "David Bowie"]
> ```
<Infobox variant="warning">
Since named entities are set by the entity recognizer, make sure to add this
component _after_ the `"ner"` component. By default, `nlp.add_pipe` will add
components to the end of the pipeline and after all other components.
</Infobox>
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| **RETURNS** | The modified `Doc` with merged entities. ~~Doc~~ |
## merge_subtokens {#merge_subtokens tag="function" new="2.1"}
Merge subtokens into a single token. Also available via the string name
`"merge_subtokens"`. As of v2.1, the parser is able to predict "subtokens" that
should be merged into one single token later on. This is especially relevant for
languages like Chinese, Japanese or Korean, where a "word" isn't defined as a
whitespace-delimited sequence of characters. Under the hood, this component uses
the [`Matcher`](/api/matcher) to find sequences of tokens with the dependency
label `"subtok"` and then merges them into a single token.
> #### Example
>
> Note that this example assumes a custom Chinese model that oversegments and
> was trained to predict subtokens.
>
> ```python
> doc = nlp("拜托")
> print([(token.text, token.dep_) for token in doc])
> # [('拜', 'subtok'), ('托', 'subtok')]
>
> nlp.add_pipe("merge_subtokens")
> doc = nlp("拜托")
> print([token.text for token in doc])
> # ['拜托']
> ```
<Infobox variant="warning">
Since subtokens are set by the parser, make sure to add this component _after_
the `"parser"` component. By default, `nlp.add_pipe` will add components to the
end of the pipeline and after all other components.
</Infobox>
| Name | Description |
| ----------- | -------------------------------------------------------------------- |
| `doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. ~~Doc~~ |
| `label` | The subtoken dependency label. Defaults to `"subtok"`. ~~str~~ |
| **RETURNS** | The modified `Doc` with merged subtokens. ~~Doc~~ |