mirror of https://github.com/explosion/spaCy.git
68 lines
1.9 KiB
ReStructuredText
68 lines
1.9 KiB
ReStructuredText
===============
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Processing Text
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===============
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The text processing API is very small and simple. Everything is a callable object,
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and you will almost always apply the pipeline all at once.
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Applying a pipeline
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-------------------
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.. py:method:: English.__call__(text, tag=True, parse=True, entity=True) --> Doc
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text (unicode)
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The text to be processed. No pre-processing needs to be applied, and any
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length of text can be submitted. Usually you will submit a whole document.
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Text may be zero-length. An exception is raised if byte strings are supplied.
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tag (bool)
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Whether to apply the part-of-speech tagger. Required for parsing and entity recognition.
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parse (bool)
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Whether to apply the syntactic dependency parser.
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entity (bool)
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Whether to apply the named entity recognizer.
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**Examples**
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>>> from spacy.en import English
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>>> nlp = English()
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>>> doc = nlp(u'Some text.) # Applies tagger, parser, entity
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>>> doc = nlp(u'Some text.', parse=False) # Applies tagger and entity, not parser
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>>> doc = nlp(u'Some text.', entity=False) # Applies tagger and parser, not entity
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>>> doc = nlp(u'Some text.', tag=False) # Does not apply tagger, entity or parser
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>>> doc = nlp(u'') # Zero-length tokens, not an error
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>>> doc = nlp(b'Some text') # Error: need unicode
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Traceback (most recent call last):
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File "<stdin>", line 1, in <module>
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File "spacy/en/__init__.py", line 128, in __call__
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tokens = self.tokenizer(text)
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TypeError: Argument 'string' has incorrect type (expected unicode, got str)
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>>> doc = nlp(b'Some text'.decode('utf8')) # Encode to unicode first.
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>>>
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Tokenizer
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---------
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.. autoclass:: spacy.tokenizer.Tokenizer
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:members:
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Tagger
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------
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.. autoclass:: spacy.en.pos.EnPosTagger
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:members:
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Parser and Entity Recognizer
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----------------------------
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.. autoclass:: spacy.syntax.parser.Parser
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:members:
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