spaCy/website/usage/_linguistic-features/_sentence-segmentation.jade

//- 💫 DOCS > USAGE > LINGUISTIC FEATURES > SENTENCE SEGMENTATION

p
    |  A #[+api("doc") #[code Doc]] object's sentences are available via the
    |  #[code Doc.sents] property. Unlike other libraries, spaCy uses the
    |  dependency parse to determine sentence boundaries. This is usually more
    |  accurate than a rule-based approach, but it also means you'll need a
    |  #[strong statistical model] and accurate predictions. If your
    |  texts are closer to general-purpose news or web text, this should work
    |  well out-of-the-box. For social media or conversational text that
    |  doesn't follow the same rules, your application may benefit from a custom
    |  rule-based implementation. You can either plug a rule-based component
    |  into your #[+a("/usage/processing-pipelines") processing pipeline] or use
    |  the #[code SentenceSegmenter] component with a custom stategy.

+h(3, "sbd-parser") Default: Using the dependency parse
    +tag-model("dependency parser")

p
    |  To view a #[code Doc]'s sentences, you can iterate over the
    |  #[code Doc.sents], a generator that yields
    |  #[+api("span") #[code Span]] objects.

+code-exec.
    import spacy

    nlp = spacy.load('en_core_web_sm')
    doc = nlp(u"This is a sentence. This is another sentence.")
    for sent in doc.sents:
        print(sent.text)

+h(3, "sbd-manual") Setting boundaries manually

p
    |  spaCy's dependency parser respects already set boundaries, so you can
    |  preprocess your #[code Doc] using custom rules #[code before] it's
    |  parsed. This can be done by adding a
    |  #[+a("/usage/processing-pipelines") custom pipeline component]. Depending
    |  on your text, this may also improve accuracy, since the parser is
    |  constrained to predict parses consistent with the sentence boundaries.

+infobox("Important note", "⚠️")
    |  To prevent inconsitent state, you can only set boundaries #[em before] a
    |  document is parsed (and #[code Doc.is_parsed] is #[code False]). To
    |  ensure that your component is added in the right place, you can set
    |  #[code before='parser'] or #[code first=True] when adding it to the
    |  pipeline using #[+api("language#add_pipe") #[code nlp.add_pipe]].

p
    |  Here's an example of a component that implements a pre-processing rule
    |  for splitting on #[code '...'] tokens. The component is added before
    |  the parser, which is then used to further segment the text. This
    |  approach can be useful if you want to implement #[em additional] rules
    |  specific to your data, while still being able to take advantage of
    |  dependency-based sentence segmentation.

+code-exec.
    import spacy

    text = u"this is a sentence...hello...and another sentence."

    nlp = spacy.load('en_core_web_sm')
    doc = nlp(text)
    print('Before:', [sent.text for sent in doc.sents])

    def set_custom_boundaries(doc):
        for token in doc[:-1]:
            if token.text == '...':
                doc[token.i+1].is_sent_start = True
        return doc

    nlp.add_pipe(set_custom_boundaries, before='parser')
    doc = nlp(text)
    print('After:', [sent.text for sent in doc.sents])

+h(3, "sbd-component") Rule-based pipeline component

p
    |  The #[code sentencizer] component is a
    |  #[+a("/usage/processing-pipelines") pipeline component] that splits
    |  sentences on punctuation like #[code .], #[code !] or #[code ?].
    |  You can plug it into your pipeline if you only need sentence boundaries
    |  without the dependency parse. Note that #[code Doc.sents] will
    |  #[strong raise an error] if no sentence boundaries are set.

+code-exec.
    import spacy
    from spacy.lang.en import English

    nlp = English()  # just the language with no model
    sbd = nlp.create_pipe('sentencizer')   # or: nlp.create_pipe('sbd')
    nlp.add_pipe(sbd)
    doc = nlp(u"This is a sentence. This is another sentence.")
    for sent in doc.sents:
        print(sent.text)

+h(3, "sbd-custom") Custom rule-based strategy

p
    |  If you want to implement your own strategy that differs from the default
    |  rule-based approach of splitting on sentences, you can also instantiate
    |  the #[code SentenceSegmenter] directly and pass in your own strategy.
    |  The strategy should be a function that takes a #[code Doc] object and
    |  yields a #[code Span] for each sentence. Here's an example of a custom
    |  segmentation strategy for splitting on newlines only:

+code-exec.
    from spacy.lang.en import English
    from spacy.pipeline import SentenceSegmenter

    def split_on_newlines(doc):
        start = 0
        seen_newline = False
        for word in doc:
            if seen_newline and not word.is_space:
                yield doc[start:word.i]
                start = word.i
                seen_newline = False
            elif word.text == '\n':
                seen_newline = True
        if start < len(doc):
            yield doc[start:len(doc)]

    nlp = English()  # just the language with no model
    sbd = SentenceSegmenter(nlp.vocab, strategy=split_on_newlines)
    nlp.add_pipe(sbd)
    doc = nlp(u"This is a sentence\n\nThis is another sentence\nAnd more")
    for sent in doc.sents:
        print([token.text for token in sent])