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spaCy/website/docs/api/sentencesegmenter.md
Ines Montani e597110d31
💫 Update website (#3285) 
<!--- Provide a general summary of your changes in the title. -->

## Description

The new website is implemented using [Gatsby](https://www.gatsbyjs.org) with [Remark](https://github.com/remarkjs/remark) and [MDX](https://mdxjs.com/). This allows authoring content in **straightforward Markdown** without the usual limitations. Standard elements can be overwritten with powerful [React](http://reactjs.org/) components and wherever Markdown syntax isn't enough, JSX components can be used. Hopefully, this update will also make it much easier to contribute to the docs. Once this PR is merged, I'll implement auto-deployment via [Netlify](https://netlify.com) on a specific branch (to avoid building the website on every PR). There's a bunch of other cool stuff that the new setup will allow us to do – including writing front-end tests, service workers, offline support, implementing a search and so on.

This PR also includes various new docs pages and content.
Resolves #3270. Resolves #3222. Resolves #2947. Resolves #2837.


### Types of change
enhancement

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
2019-02-17 19:31:19 +01:00

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title tag source
SentenceSegmenter class spacy/pipeline.pyx

A simple spaCy hook, to allow custom sentence boundary detection logic that doesn't require the dependency parse. By default, sentence segmentation is performed by the DependencyParser, so the SentenceSegmenter lets you implement a simpler, rule-based strategy that doesn't require a statistical model to be loaded. The component is also available via the string name "sentencizer". After initialization, it is typically added to the processing pipeline using nlp.add_pipe.

SentenceSegmenter.__init__

Initialize the sentence segmenter. To change the sentence boundary detection strategy, pass a generator function strategy on initialization, or assign a new strategy to the .strategy attribute. Sentence detection strategies should be generators that take Doc objects and yield Span objects for each sentence.

Example

# Construction via create_pipe
sentencizer = nlp.create_pipe("sentencizer")

# Construction from class
from spacy.pipeline import SentenceSegmenter
sentencizer = SentenceSegmenter(nlp.vocab)
Name Type Description
vocab Vocab The shared vocabulary.
strategy unicode / callable The segmentation strategy to use. Defaults to "on_punct".
RETURNS SentenceSegmenter The newly constructed object.

SentenceSegmenter.__call__

Apply the sentence segmenter on a Doc. Typically, this happens automatically after the component has been added to the pipeline using nlp.add_pipe.

Example

from spacy.lang.en import English

nlp = English()
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer)
doc = nlp(u"This is a sentence. This is another sentence.")
assert list(doc.sents) == 2
Name Type Description
doc Doc The Doc object to process, e.g. the Doc in the pipeline.
RETURNS Doc The modified Doc with added sentence boundaries.

SentenceSegmenter.split_on_punct

Split the Doc on punctuation characters ., ! and ?. This is the default strategy used by the SentenceSegmenter.

Name Type Description
doc Doc The Doc object to process.
YIELDS Span The sentences in the document.

Attributes

Name Type Description
strategy callable The segmentation strategy. Can be overwritten after initialization.