2016-10-31 21:04:15 +03:00
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//- 💫 DOCS > API > TOKEN
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2017-10-03 15:27:22 +03:00
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include ../_includes/_mixins
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2016-10-31 21:04:15 +03:00
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p An individual token — i.e. a word, punctuation symbol, whitespace, etc.
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2017-05-19 19:47:56 +03:00
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+h(2, "init") Token.__init__
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+tag method
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p Construct a #[code Token] object.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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assert token.text == u'Give'
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell A storage container for lexical types.
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+row
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+cell #[code doc]
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+cell #[code Doc]
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+cell The parent document.
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+row
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+cell #[code offset]
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+cell int
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+cell The index of the token within the document.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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+cell returns
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+cell #[code Token]
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+cell The newly constructed object.
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+h(2, "len") Token.__len__
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+tag method
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p The number of unicode characters in the token, i.e. #[code token.text].
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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assert len(token) == 4
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell int
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+cell The number of unicode characters in the token.
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2017-10-10 05:23:37 +03:00
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+h(2, "set_extension") Token.set_extension
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+tag classmethod
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+tag-new(2)
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p
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| Define a custom attribute on the #[code Token] which becomes available
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| via #[code Token._]. For details, see the documentation on
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| #[+a("/usage/processing-pipelines#custom-components-attributes") custom attributes].
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+aside-code("Example").
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2017-10-11 03:30:40 +03:00
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from spacy.tokens import Token
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2017-10-10 05:23:37 +03:00
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fruit_getter = lambda token: token.text in ('apple', 'pear', 'banana')
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Token.set_extension('is_fruit', getter=fruit_getter)
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doc = nlp(u'I have an apple')
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assert doc[3]._.is_fruit
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell
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| Name of the attribute to set by the extension. For example,
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| #[code 'my_attr'] will be available as #[code token._.my_attr].
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+row
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+cell #[code default]
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+cell -
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+cell
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| Optional default value of the attribute if no getter or method
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| is defined.
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+row
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+cell #[code method]
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+cell callable
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+cell
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| Set a custom method on the object, for example
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| #[code token._.compare(other_token)].
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+row
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+cell #[code getter]
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+cell callable
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+cell
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| Getter function that takes the object and returns an attribute
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| value. Is called when the user accesses the #[code ._] attribute.
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+row
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+cell #[code setter]
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+cell callable
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+cell
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| Setter function that takes the #[code Token] and a value, and
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| modifies the object. Is called when the user writes to the
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| #[code Token._] attribute.
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+h(2, "get_extension") Token.get_extension
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+tag classmethod
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+tag-new(2)
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p
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| Look up a previously registered extension by name. Returns a 4-tuple
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| #[code.u-break (default, method, getter, setter)] if the extension is
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| registered. Raises a #[code KeyError] otherwise.
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+aside-code("Example").
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2017-10-11 03:30:40 +03:00
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from spacy.tokens import Token
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Token.set_extension('is_fruit', default=False)
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extension = Token.get_extension('is_fruit')
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assert extension == (False, None, None, None)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension.
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+row("foot")
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+cell returns
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+cell tuple
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+cell
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| A #[code.u-break (default, method, getter, setter)] tuple of the
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| extension.
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+h(2, "has_extension") Token.has_extension
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+tag classmethod
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+tag-new(2)
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p Check whether an extension has been registered on the #[code Token] class.
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+aside-code("Example").
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2017-10-11 03:30:40 +03:00
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from spacy.tokens import Token
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2017-10-10 05:23:37 +03:00
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Token.set_extension('is_fruit', default=False)
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assert Token.has_extension('is_fruit')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension to check.
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+row("foot")
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+cell returns
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+cell bool
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+cell Whether the extension has been registered.
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2018-07-21 16:51:28 +03:00
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+h(2, "remove_extension") Token.remove_extension
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+tag classmethod
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+tag-new("2.0.11")
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p Remove a previously registered extension.
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+aside-code("Example").
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from spacy.tokens import Token
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Token.set_extension('is_fruit', default=False)
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removed = Token.remove_extension('is_fruit')
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assert not Token.has_extension('is_fruit')
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code name]
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+cell unicode
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+cell Name of the extension.
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+row("foot")
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+cell returns
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+cell tuple
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+cell
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| A #[code.u-break (default, method, getter, setter)] tuple of the
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| removed extension.
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2017-05-19 19:47:56 +03:00
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+h(2, "check_flag") Token.check_flag
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+tag method
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p Check the value of a boolean flag.
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+aside-code("Example").
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from spacy.attrs import IS_TITLE
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doc = nlp(u'Give it back! He pleaded.')
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token = doc[0]
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assert token.check_flag(IS_TITLE) == True
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to check.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell returns
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+cell bool
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+cell Whether the flag is set.
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+h(2, "similarity") Token.similarity
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+tag method
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2017-05-19 21:24:46 +03:00
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+tag-model("vectors")
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2017-05-19 19:47:56 +03:00
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+aside-code("Example").
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apples, _, oranges = nlp(u'apples and oranges')
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apples_oranges = apples.similarity(oranges)
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oranges_apples = oranges.similarity(apples)
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assert apples_oranges == oranges_apples
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+table(["Name", "Type", "Description"])
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+row
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+cell other
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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2017-05-19 20:59:02 +03:00
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+h(2, "nbor") Token.nbor
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2017-05-19 19:47:56 +03:00
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+tag method
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2017-05-19 20:59:02 +03:00
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p Get a neighboring token.
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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give_nbor = doc[0].nbor()
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assert give_nbor.text == u'it'
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+table(["Name", "Type", "Description"])
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+row
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2017-05-19 20:59:02 +03:00
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+cell #[code i]
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+cell int
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+cell The relative position of the token to get. Defaults to #[code 1].
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2017-05-19 19:47:56 +03:00
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell returns
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2017-05-19 20:59:02 +03:00
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+cell #[code Token]
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+cell The token at position #[code self.doc[self.i+i]].
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2017-05-19 19:47:56 +03:00
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2017-05-19 20:59:02 +03:00
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+h(2, "is_ancestor") Token.is_ancestor
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+tag method
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2017-05-19 21:24:46 +03:00
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+tag-model("parse")
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2017-05-19 19:47:56 +03:00
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p
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2017-05-19 20:59:02 +03:00
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| Check whether this token is a parent, grandparent, etc. of another
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| in the dependency tree.
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2017-05-19 19:47:56 +03:00
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+aside-code("Example").
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2017-05-19 20:59:02 +03:00
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doc = nlp(u'Give it back! He pleaded.')
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give = doc[0]
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it = doc[1]
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assert give.is_ancestor(it)
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2017-05-19 19:47:56 +03:00
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+table(["Name", "Type", "Description"])
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2017-05-19 20:59:02 +03:00
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+row
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+cell descendant
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+cell #[code Token]
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+cell Another token.
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell returns
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+cell bool
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2017-05-19 20:59:02 +03:00
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+cell Whether this token is the ancestor of the descendant.
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2017-05-19 19:47:56 +03:00
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2017-05-19 20:59:02 +03:00
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+h(2, "ancestors") Token.ancestors
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2017-05-19 19:47:56 +03:00
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+tag property
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2017-05-19 21:24:46 +03:00
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+tag-model("parse")
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2017-05-19 19:47:56 +03:00
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2017-05-19 20:59:02 +03:00
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p The rightmost token of this token's syntactic descendants.
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2017-05-19 19:47:56 +03:00
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+aside-code("Example").
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2017-05-19 20:59:02 +03:00
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doc = nlp(u'Give it back! He pleaded.')
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it_ancestors = doc[1].ancestors
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assert [t.text for t in it_ancestors] == [u'Give']
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he_ancestors = doc[4].ancestors
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assert [t.text for t in he_ancestors] == [u'pleaded']
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2017-05-19 19:47:56 +03:00
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+table(["Name", "Type", "Description"])
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 20:59:02 +03:00
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+cell yields
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+cell #[code Token]
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+cell
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| A sequence of ancestor tokens such that
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| #[code ancestor.is_ancestor(self)].
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2017-05-19 19:47:56 +03:00
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+h(2, "conjuncts") Token.conjuncts
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+tag property
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2017-05-19 21:24:46 +03:00
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+tag-model("parse")
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2017-05-19 19:47:56 +03:00
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p A sequence of coordinated tokens, including the token itself.
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2017-05-19 20:59:02 +03:00
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+aside-code("Example").
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doc = nlp(u'I like apples and oranges')
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apples_conjuncts = doc[2].conjuncts
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assert [t.text for t in apples_conjuncts] == [u'oranges']
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2017-05-19 19:47:56 +03:00
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+table(["Name", "Type", "Description"])
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell yields
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+cell #[code Token]
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+cell A coordinated token.
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+h(2, "children") Token.children
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+tag property
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2017-05-19 21:24:46 +03:00
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+tag-model("parse")
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2017-05-19 19:47:56 +03:00
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p A sequence of the token's immediate syntactic children.
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2017-05-19 20:59:02 +03:00
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+aside-code("Example").
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doc = nlp(u'Give it back! He pleaded.')
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give_children = doc[0].children
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assert [t.text for t in give_children] == [u'it', u'back', u'!']
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2017-05-19 19:47:56 +03:00
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+table(["Name", "Type", "Description"])
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2017-10-03 15:27:22 +03:00
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+row("foot")
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2017-05-19 19:47:56 +03:00
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+cell yields
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+cell #[code Token]
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+cell A child token such that #[code child.head==self].
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2017-10-27 18:07:26 +03:00
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+h(2, "lefts") Token.lefts
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+tag property
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+tag-model("parse")
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p
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| The leftward immediate children of the word, in the syntactic dependency
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| parse.
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+aside-code("Example").
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doc = nlp(u'I like New York in Autumn.')
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lefts = [t.text for t in doc[3].lefts]
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assert lefts == [u'New']
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|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
|
|
+row("foot")
|
|
|
|
+cell yields
|
|
|
|
+cell #[code Token]
|
|
|
|
+cell A left-child of the token.
|
|
|
|
|
|
|
|
+h(2, "rights") Token.rights
|
|
|
|
+tag property
|
|
|
|
+tag-model("parse")
|
|
|
|
|
|
|
|
p
|
|
|
|
| The rightward immediate children of the word, in the syntactic
|
|
|
|
| dependency parse.
|
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like New York in Autumn.')
|
|
|
|
rights = [t.text for t in doc[3].rights]
|
|
|
|
assert rights == [u'in']
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
|
|
+row("foot")
|
|
|
|
+cell yields
|
|
|
|
+cell #[code Token]
|
|
|
|
+cell A right-child of the token.
|
|
|
|
|
|
|
|
+h(2, "n_lefts") Token.n_lefts
|
|
|
|
+tag property
|
|
|
|
+tag-model("parse")
|
|
|
|
|
|
|
|
p
|
|
|
|
| The number of leftward immediate children of the word, in the syntactic
|
|
|
|
| dependency parse.
|
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like New York in Autumn.')
|
|
|
|
assert doc[3].n_lefts == 1
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
|
|
+row("foot")
|
|
|
|
+cell returns
|
|
|
|
+cell int
|
|
|
|
+cell The number of left-child tokens.
|
|
|
|
|
|
|
|
+h(2, "n_rights") Token.n_rights
|
|
|
|
+tag property
|
|
|
|
+tag-model("parse")
|
|
|
|
|
|
|
|
p
|
|
|
|
| The number of rightward immediate children of the word, in the syntactic
|
|
|
|
| dependency parse.
|
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like New York in Autumn.')
|
|
|
|
assert doc[3].n_rights == 1
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
|
|
+row("foot")
|
|
|
|
+cell returns
|
|
|
|
+cell int
|
|
|
|
+cell The number of right-child tokens.
|
|
|
|
|
2017-05-19 19:47:56 +03:00
|
|
|
+h(2, "subtree") Token.subtree
|
|
|
|
+tag property
|
2017-05-19 21:24:46 +03:00
|
|
|
+tag-model("parse")
|
2017-05-19 19:47:56 +03:00
|
|
|
|
2018-08-07 11:49:21 +03:00
|
|
|
p A sequence of all the token's syntactic descendants.
|
2017-05-19 19:47:56 +03:00
|
|
|
|
2017-05-19 20:59:02 +03:00
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'Give it back! He pleaded.')
|
|
|
|
give_subtree = doc[0].subtree
|
|
|
|
assert [t.text for t in give_subtree] == [u'Give', u'it', u'back', u'!']
|
|
|
|
|
2017-05-19 19:47:56 +03:00
|
|
|
+table(["Name", "Type", "Description"])
|
2017-10-03 15:27:22 +03:00
|
|
|
+row("foot")
|
2017-05-19 19:47:56 +03:00
|
|
|
+cell yields
|
|
|
|
+cell #[code Token]
|
|
|
|
+cell A descendant token such that #[code self.is_ancestor(descendant)].
|
|
|
|
|
2017-11-01 16:13:22 +03:00
|
|
|
+h(2, "is_sent_start") Token.is_sent_start
|
|
|
|
+tag property
|
|
|
|
+tag-new(2)
|
|
|
|
|
|
|
|
p
|
|
|
|
| A boolean value indicating whether the token starts a sentence.
|
|
|
|
| #[code None] if unknown.
|
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'Give it back! He pleaded.')
|
|
|
|
assert doc[4].is_sent_start
|
|
|
|
assert not doc[5].is_sent_start
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
|
|
|
+row("foot")
|
|
|
|
+cell returns
|
|
|
|
+cell bool
|
|
|
|
+cell Whether the token starts a sentence.
|
|
|
|
|
2017-11-07 14:00:43 +03:00
|
|
|
+infobox("Changed in v2.0", "⚠️")
|
2017-11-01 16:13:22 +03:00
|
|
|
| As of spaCy v2.0, the #[code Token.sent_start] property is deprecated and
|
|
|
|
| has been replaced with #[code Token.is_sent_start], which returns a
|
|
|
|
| boolean value instead of a misleading #[code 0] for #[code False] and
|
|
|
|
| #[code 1] for #[code True]. It also now returns #[code None] if the
|
|
|
|
| answer is unknown, and fixes a quirk in the old logic that would always
|
|
|
|
| set the property to #[code 0] for the first word of the document.
|
|
|
|
|
|
|
|
+code-wrapper
|
|
|
|
+code-new assert doc[4].is_sent_start == True
|
|
|
|
+code-old assert doc[4].sent_start == 1
|
|
|
|
|
2017-05-19 20:59:02 +03:00
|
|
|
+h(2, "has_vector") Token.has_vector
|
2017-05-19 19:47:56 +03:00
|
|
|
+tag property
|
2017-05-19 21:24:46 +03:00
|
|
|
+tag-model("vectors")
|
2017-05-19 19:47:56 +03:00
|
|
|
|
2017-05-19 20:59:02 +03:00
|
|
|
p
|
|
|
|
| A boolean value indicating whether a word vector is associated with the
|
|
|
|
| token.
|
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like apples')
|
|
|
|
apples = doc[2]
|
|
|
|
assert apples.has_vector
|
2017-05-19 19:47:56 +03:00
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
2017-10-03 15:27:22 +03:00
|
|
|
+row("foot")
|
2017-05-19 20:59:02 +03:00
|
|
|
+cell returns
|
|
|
|
+cell bool
|
|
|
|
+cell Whether the token has a vector data attached.
|
|
|
|
|
|
|
|
+h(2, "vector") Token.vector
|
|
|
|
+tag property
|
2017-05-19 21:24:46 +03:00
|
|
|
+tag-model("vectors")
|
2017-05-19 20:59:02 +03:00
|
|
|
|
2017-05-20 16:13:33 +03:00
|
|
|
p A real-valued meaning representation.
|
2017-05-19 20:59:02 +03:00
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like apples')
|
|
|
|
apples = doc[2]
|
|
|
|
assert apples.vector.dtype == 'float32'
|
|
|
|
assert apples.vector.shape == (300,)
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
2017-10-03 15:27:22 +03:00
|
|
|
+row("foot")
|
2017-05-19 20:59:02 +03:00
|
|
|
+cell returns
|
2017-08-19 13:44:23 +03:00
|
|
|
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
|
2017-05-19 20:59:02 +03:00
|
|
|
+cell A 1D numpy array representing the token's semantics.
|
|
|
|
|
2017-12-17 15:32:19 +03:00
|
|
|
+h(2, "vector_norm") Token.vector_norm
|
2017-05-19 20:59:02 +03:00
|
|
|
+tag property
|
2017-05-19 21:24:46 +03:00
|
|
|
+tag-model("vectors")
|
2017-05-19 20:59:02 +03:00
|
|
|
|
2017-05-20 16:13:33 +03:00
|
|
|
p The L2 norm of the token's vector representation.
|
2017-05-19 20:59:02 +03:00
|
|
|
|
|
|
|
+aside-code("Example").
|
|
|
|
doc = nlp(u'I like apples and pasta')
|
|
|
|
apples = doc[2]
|
|
|
|
pasta = doc[4]
|
|
|
|
apples.vector_norm # 6.89589786529541
|
|
|
|
pasta.vector_norm # 7.759851932525635
|
|
|
|
assert apples.vector_norm != pasta.vector_norm
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
2017-10-03 15:27:22 +03:00
|
|
|
+row("foot")
|
2017-05-19 20:59:02 +03:00
|
|
|
+cell returns
|
|
|
|
+cell float
|
|
|
|
+cell The L2 norm of the vector representation.
|
2017-05-19 19:47:56 +03:00
|
|
|
|
2016-10-31 21:04:15 +03:00
|
|
|
+h(2, "attributes") Attributes
|
|
|
|
|
|
|
|
+table(["Name", "Type", "Description"])
|
2018-07-21 16:51:44 +03:00
|
|
|
+row
|
|
|
|
+cell #[code doc]
|
|
|
|
+cell #[code Doc]
|
|
|
|
+cell The parent document.
|
|
|
|
|
|
|
|
+row
|
|
|
|
+cell #[code sent]
|
|
|
|
+tag-new("2.0.12")
|
|
|
|
+cell #[code Span]
|
|
|
|
+cell The sentence span that this token is a part of.
|
|
|
|
|
2017-05-19 19:47:56 +03:00
|
|
|
+row
|
|
|
|
+cell #[code text]
|
|
|
|
+cell unicode
|
|
|
|
+cell Verbatim text content.
|
2017-10-03 15:27:22 +03:00
|
|
|
|
2017-05-19 19:47:56 +03:00
|
|
|
+row
|
|
|
|
+cell #[code text_with_ws]
|
|
|
|
+cell unicode
|
|
|
|
+cell Text content, with trailing space character if present.
|
|
|
|
|
|
|
|
+row
|
|
|
|
+cell #[code whitespace_]
|
|
|
|
+cell unicode
|
|
|
|
+cell Trailing space character if present.
|
|
|
|
|
2017-10-03 15:27:22 +03:00
|
|
|
+row
|
|
|
|
+cell #[code orth]
|
|
|
|
+cell int
|
|
|
|
+cell ID of the verbatim text content.
|
|
|
|
|
|
|
|
+row
|
|
|
|
+cell #[code orth_]
|
|
|
|
+cell unicode
|
|
|
|
+cell
|
💫 Port master changes over to develop (#2979)
* Create aryaprabhudesai.md (#2681)
* Update _install.jade (#2688)
Typo fix: "models" -> "model"
* Add FAC to spacy.explain (resolves #2706)
* Remove docstrings for deprecated arguments (see #2703)
* When calling getoption() in conftest.py, pass a default option (#2709)
* When calling getoption() in conftest.py, pass a default option
This is necessary to allow testing an installed spacy by running:
pytest --pyargs spacy
* Add contributor agreement
* update bengali token rules for hyphen and digits (#2731)
* Less norm computations in token similarity (#2730)
* Less norm computations in token similarity
* Contributor agreement
* Remove ')' for clarity (#2737)
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* added contributor agreement for mbkupfer (#2738)
* Basic support for Telugu language (#2751)
* Lex _attrs for polish language (#2750)
* Signed spaCy contributor agreement
* Added polish version of english lex_attrs
* Introduces a bulk merge function, in order to solve issue #653 (#2696)
* Fix comment
* Introduce bulk merge to increase performance on many span merges
* Sign contributor agreement
* Implement pull request suggestions
* Describe converters more explicitly (see #2643)
* Add multi-threading note to Language.pipe (resolves #2582) [ci skip]
* Fix formatting
* Fix dependency scheme docs (closes #2705) [ci skip]
* Don't set stop word in example (closes #2657) [ci skip]
* Add words to portuguese language _num_words (#2759)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Update Indonesian model (#2752)
* adding e-KTP in tokenizer exceptions list
* add exception token
* removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception
* add tokenizer exceptions list
* combining base_norms with norm_exceptions
* adding norm_exception
* fix double key in lemmatizer
* remove unused import on punctuation.py
* reformat stop_words to reduce number of lines, improve readibility
* updating tokenizer exception
* implement is_currency for lang/id
* adding orth_first_upper in tokenizer_exceptions
* update the norm_exception list
* remove bunch of abbreviations
* adding contributors file
* Fixed spaCy+Keras example (#2763)
* bug fixes in keras example
* created contributor agreement
* Adding French hyphenated first name (#2786)
* Fix typo (closes #2784)
* Fix typo (#2795) [ci skip]
Fixed typo on line 6 "regcognizer --> recognizer"
* Adding basic support for Sinhala language. (#2788)
* adding Sinhala language package, stop words, examples and lex_attrs.
* Adding contributor agreement
* Updating contributor agreement
* Also include lowercase norm exceptions
* Fix error (#2802)
* Fix error
ValueError: cannot resize an array that references or is referenced
by another array in this way. Use the resize function
* added spaCy Contributor Agreement
* Add charlax's contributor agreement (#2805)
* agreement of contributor, may I introduce a tiny pl languge contribution (#2799)
* Contributors agreement
* Contributors agreement
* Contributors agreement
* Add jupyter=True to displacy.render in documentation (#2806)
* Revert "Also include lowercase norm exceptions"
This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e.
* Remove deprecated encoding argument to msgpack
* Set up dependency tree pattern matching skeleton (#2732)
* Fix bug when too many entity types. Fixes #2800
* Fix Python 2 test failure
* Require older msgpack-numpy
* Restore encoding arg on msgpack-numpy
* Try to fix version pin for msgpack-numpy
* Update Portuguese Language (#2790)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols
* Extended punctuation and norm_exceptions in the Portuguese language
* Correct error in spacy universe docs concerning spacy-lookup (#2814)
* Update Keras Example for (Parikh et al, 2016) implementation (#2803)
* bug fixes in keras example
* created contributor agreement
* baseline for Parikh model
* initial version of parikh 2016 implemented
* tested asymmetric models
* fixed grevious error in normalization
* use standard SNLI test file
* begin to rework parikh example
* initial version of running example
* start to document the new version
* start to document the new version
* Update Decompositional Attention.ipynb
* fixed calls to similarity
* updated the README
* import sys package duh
* simplified indexing on mapping word to IDs
* stupid python indent error
* added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround
* Fix typo (closes #2815) [ci skip]
* Update regex version dependency
* Set version to 2.0.13.dev3
* Skip seemingly problematic test
* Remove problematic test
* Try previous version of regex
* Revert "Remove problematic test"
This reverts commit bdebbef45552d698d390aa430b527ee27830f11b.
* Unskip test
* Try older version of regex
* 💫 Update training examples and use minibatching (#2830)
<!--- Provide a general summary of your changes in the title. -->
## Description
Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results.
### Types of change
enhancements
## 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.
* Visual C++ link updated (#2842) (closes #2841) [ci skip]
* New landing page
* Add contribution agreement
* Correcting lang/ru/examples.py (#2845)
* Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement
* Correct some grammatical inaccuracies in lang\ru\examples.py
* Move contributor agreement to separate file
* Set version to 2.0.13.dev4
* Add Persian(Farsi) language support (#2797)
* Also include lowercase norm exceptions
* Remove in favour of https://github.com/explosion/spaCy/graphs/contributors
* Rule-based French Lemmatizer (#2818)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class.
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
- Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version.
- Add several files containing exhaustive list of words for each part of speech
- Add some lemma rules
- Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX
- Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned
- Modify the lemmatize function to check in lookup table as a last resort
- Init files are updated so the model can support all the functionalities mentioned above
- Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py
## 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.
* Set version to 2.0.13
* Fix formatting and consistency
* Update docs for new version [ci skip]
* Increment version [ci skip]
* Add info on wheels [ci skip]
* Adding "This is a sentence" example to Sinhala (#2846)
* Add wheels badge
* Update badge [ci skip]
* Update README.rst [ci skip]
* Update murmurhash pin
* Increment version to 2.0.14.dev0
* Update GPU docs for v2.0.14
* Add wheel to setup_requires
* Import prefer_gpu and require_gpu functions from Thinc
* Add tests for prefer_gpu() and require_gpu()
* Update requirements and setup.py
* Workaround bug in thinc require_gpu
* Set version to v2.0.14
* Update push-tag script
* Unhack prefer_gpu
* Require thinc 6.10.6
* Update prefer_gpu and require_gpu docs [ci skip]
* Fix specifiers for GPU
* Set version to 2.0.14.dev1
* Set version to 2.0.14
* Update Thinc version pin
* Increment version
* Fix msgpack-numpy version pin
* Increment version
* Update version to 2.0.16
* Update version [ci skip]
* Redundant ')' in the Stop words' example (#2856)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [ ] I have submitted the spaCy Contributor Agreement.
- [ ] I ran the tests, and all new and existing tests passed.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Documentation improvement regarding joblib and SO (#2867)
Some documentation improvements
## Description
1. Fixed the dead URL to joblib
2. Fixed Stack Overflow brand name (with space)
### Types of change
Documentation
## 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.
* raise error when setting overlapping entities as doc.ents (#2880)
* Fix out-of-bounds access in NER training
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
* Change PyThaiNLP Url (#2876)
* Fix missing comma
* Add example showing a fix-up rule for space entities
* Set version to 2.0.17.dev0
* Update regex version
* Revert "Update regex version"
This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a.
* Try setting older regex version, to align with conda
* Set version to 2.0.17
* Add spacy-js to universe [ci-skip]
* Add spacy-raspberry to universe (closes #2889)
* Add script to validate universe json [ci skip]
* Removed space in docs + added contributor indo (#2909)
* - removed unneeded space in documentation
* - added contributor info
* Allow input text of length up to max_length, inclusive (#2922)
* Include universe spec for spacy-wordnet component (#2919)
* feat: include universe spec for spacy-wordnet component
* chore: include spaCy contributor agreement
* Minor formatting changes [ci skip]
* Fix image [ci skip]
Twitter URL doesn't work on live site
* Check if the word is in one of the regular lists specific to each POS (#2886)
* 💫 Create random IDs for SVGs to prevent ID clashes (#2927)
Resolves #2924.
## Description
Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.)
### Types of change
bug fix
## 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.
* Fix typo [ci skip]
* fixes symbolic link on py3 and windows (#2949)
* fixes symbolic link on py3 and windows
during setup of spacy using command
python -m spacy link en_core_web_sm en
closes #2948
* Update spacy/compat.py
Co-Authored-By: cicorias <cicorias@users.noreply.github.com>
* Fix formatting
* Update universe [ci skip]
* Catalan Language Support (#2940)
* Catalan language Support
* Ddding Catalan to documentation
* Sort languages alphabetically [ci skip]
* Update tests for pytest 4.x (#2965)
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize))
- [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here)
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## 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.
* Fix regex pin to harmonize with conda (#2964)
* Update README.rst
* Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977)
Fixes #2976
* Fix typo
* Fix typo
* Remove duplicate file
* Require thinc 7.0.0.dev2
Fixes bug in gpu_ops that would use cupy instead of numpy on CPU
* Add missing import
* Fix error IDs
* Fix tests
2018-11-29 18:30:29 +03:00
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| Verbatim text content (identical to #[code Token.text]). Exists
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2017-10-03 15:27:22 +03:00
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| mostly for consistency with the other attributes.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The vocab object of the parent #[code Doc].
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+row
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+cell #[code doc]
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+cell #[code Doc]
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+cell The parent document.
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2017-05-19 19:47:56 +03:00
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+row
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+cell #[code head]
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+cell #[code Token]
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+cell The syntactic parent, or "governor", of this token.
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+row
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+cell #[code left_edge]
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+cell #[code Token]
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+cell The leftmost token of this token's syntactic descendants.
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+row
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+cell #[code right_edge]
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+cell #[code Token]
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2018-08-07 11:49:21 +03:00
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+cell The rightmost token of this token's syntactic descendants.
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2017-05-19 19:47:56 +03:00
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code i]
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+cell int
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+cell The index of the token within the parent document.
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2017-05-19 19:47:56 +03:00
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code ent_type]
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+cell int
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+cell Named entity type.
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2017-05-19 19:47:56 +03:00
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code ent_type_]
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+cell unicode
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+cell Named entity type.
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+row
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+cell #[code ent_iob]
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+cell int
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+cell
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2017-05-24 00:15:50 +03:00
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| IOB code of named entity tag. #[code "B"]
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| means the token begins an entity, #[code "I"] means it is inside
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| an entity, #[code "O"] means it is outside an entity, and
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| #[code ""] means no entity tag is set.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code ent_iob_]
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+cell unicode
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+cell
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| IOB code of named entity tag. #[code "B"]
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2017-05-19 19:47:56 +03:00
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| means the token begins an entity, #[code "I"] means it is inside
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| an entity, #[code "O"] means it is outside an entity, and
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2016-10-31 21:04:15 +03:00
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| #[code ""] means no entity tag is set.
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+row
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+cell #[code ent_id]
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+cell int
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2017-05-19 19:47:56 +03:00
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+cell
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2018-06-11 18:47:46 +03:00
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| ID of the entity the token is an instance of, if any. Currently
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| not used, but potentially for coreference resolution.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code ent_id_]
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+cell unicode
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2017-05-19 19:47:56 +03:00
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+cell
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2018-06-11 18:47:46 +03:00
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| ID of the entity the token is an instance of, if any. Currently
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| not used, but potentially for coreference resolution.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lemma]
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+cell int
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+cell
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2017-05-26 13:43:16 +03:00
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| Base form of the token, with no inflectional suffixes.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lemma_]
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+cell unicode
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2017-05-26 13:43:16 +03:00
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+cell Base form of the token, with no inflectional suffixes.
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2016-10-31 21:04:15 +03:00
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2017-10-27 16:41:45 +03:00
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+row
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+cell #[code norm]
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+cell int
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+cell
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| The token's norm, i.e. a normalised form of the token text.
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|
| Usually set in the language's
|
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| #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
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| #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
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+row
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+cell #[code norm_]
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+cell unicode
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+cell
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| The token's norm, i.e. a normalised form of the token text.
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| Usually set in the language's
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| #[+a("/usage/adding-languages#tokenizer-exceptions") tokenizer exceptions] or
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| #[+a("/usage/adding-languages#norm-exceptions") norm exceptions].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lower]
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+cell int
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2017-10-27 16:41:45 +03:00
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+cell Lowercase form of the token.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lower_]
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+cell unicode
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2017-10-27 16:41:45 +03:00
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+cell
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| Lowercase form of the token text. Equivalent to
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| #[code Token.text.lower()].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code shape]
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+cell int
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2017-05-26 13:43:16 +03:00
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+cell
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| Transform of the tokens's string, to show orthographic features.
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| For example, "Xxxx" or "dd".
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code shape_]
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+cell unicode
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2017-05-28 20:25:34 +03:00
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+cell
|
2017-05-26 13:43:16 +03:00
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| Transform of the tokens's string, to show orthographic features.
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| For example, "Xxxx" or "dd".
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code prefix]
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+cell int
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2017-05-28 20:25:34 +03:00
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+cell
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| Hash value of a length-N substring from the start of the
|
2017-05-26 13:43:16 +03:00
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| token. Defaults to #[code N=1].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code prefix_]
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+cell unicode
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+cell
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2017-05-26 13:43:16 +03:00
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| A length-N substring from the start of the token. Defaults to
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2016-10-31 21:04:15 +03:00
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| #[code N=1].
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+row
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+cell #[code suffix]
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+cell int
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+cell
|
2017-05-28 20:25:34 +03:00
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| Hash value of a length-N substring from the end of the token.
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| Defaults to #[code N=3].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code suffix_]
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+cell unicode
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2017-10-27 16:41:45 +03:00
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+cell
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| Length-N substring from the end of the token. Defaults to
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| #[code N=3].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_alpha]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Does the token consist of alphabetic characters? Equivalent to
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| #[code token.text.isalpha()].
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_ascii]
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+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Does the token consist of ASCII characters? Equivalent to
|
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| #[code [any(ord(c) >= 128 for c in token.text)]].
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2016-10-31 21:04:15 +03:00
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+row
|
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+cell #[code is_digit]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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| Does the token consist of digits? Equivalent to
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| #[code token.text.isdigit()].
|
2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_lower]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell
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|
| Is the token in lowercase? Equivalent to
|
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| #[code token.text.islower()].
|
2016-10-31 21:04:15 +03:00
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|
2017-10-07 16:04:16 +03:00
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+row
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+cell #[code is_upper]
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|
+cell bool
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|
+cell
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| Is the token in uppercase? Equivalent to
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| #[code token.text.isupper()].
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|
2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_title]
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|
+cell bool
|
2017-05-26 13:43:16 +03:00
|
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|
+cell
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|
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|
| Is the token in titlecase? Equivalent to
|
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| #[code token.text.istitle()].
|
2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_punct]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
|
|
|
+cell Is the token punctuation?
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2016-10-31 21:04:15 +03:00
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|
2017-10-20 14:08:44 +03:00
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+row
|
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+cell #[code is_left_punct]
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|
+cell bool
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|
+cell Is the token a left punctuation mark, e.g. #[code (]?
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+row
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+cell #[code is_right_punct]
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|
+cell bool
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|
+cell Is the token a right punctuation mark, e.g. #[code )]?
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|
2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_space]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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|
+cell
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|
| Does the token consist of whitespace characters? Equivalent to
|
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|
|
| #[code token.text.isspace()].
|
2016-10-31 21:04:15 +03:00
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2017-10-20 14:08:44 +03:00
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+row
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+cell #[code is_bracket]
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|
+cell bool
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|
+cell Is the token a bracket?
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+row
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+cell #[code is_quote]
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|
+cell bool
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+cell Is the token a quotation mark?
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|
2018-03-24 19:12:48 +03:00
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+row
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+cell #[code is_currency]
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+tag-new("2.0.8")
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|
+cell bool
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|
+cell Is the token a currency symbol?
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code like_url]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell Does the token resemble a URL?
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code like_num]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell Does the token represent a number? e.g. "10.9", "10", "ten", etc.
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code like_email]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell Does the token resemble an email address?
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_oov]
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+cell bool
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2017-05-26 13:43:16 +03:00
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+cell Is the token out-of-vocabulary?
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code is_stop]
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|
+cell bool
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2017-05-26 13:43:16 +03:00
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+cell Is the token part of a "stop list"?
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code pos]
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+cell int
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+cell Coarse-grained part-of-speech.
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+row
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+cell #[code pos_]
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+cell unicode
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|
+cell Coarse-grained part-of-speech.
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+row
|
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+cell #[code tag]
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+cell int
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+cell Fine-grained part-of-speech.
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+row
|
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+cell #[code tag_]
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|
|
+cell unicode
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|
+cell Fine-grained part-of-speech.
|
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|
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+row
|
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+cell #[code dep]
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+cell int
|
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|
|
+cell Syntactic dependency relation.
|
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|
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+row
|
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+cell #[code dep_]
|
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|
+cell unicode
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|
+cell Syntactic dependency relation.
|
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+row
|
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|
+cell #[code lang]
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+cell int
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+cell Language of the parent document's vocabulary.
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2017-10-27 16:41:45 +03:00
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2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lang_]
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+cell unicode
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+cell Language of the parent document's vocabulary.
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+row
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+cell #[code prob]
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+cell float
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+cell Smoothed log probability estimate of token's type.
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+row
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+cell #[code idx]
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+cell int
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+cell The character offset of the token within the parent document.
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+row
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+cell #[code sentiment]
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+cell float
|
2017-10-27 18:07:26 +03:00
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+cell
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| A scalar value indicating the positivity or negativity of the
|
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| token.
|
2016-10-31 21:04:15 +03:00
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+row
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+cell #[code lex_id]
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+cell int
|
2017-10-27 18:07:26 +03:00
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|
+cell Sequential ID of the token's lexical type.
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+row
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+cell #[code rank]
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+cell int
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+cell
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| Sequential ID of the token's lexical type, used to index into
|
2017-10-27 22:07:50 +03:00
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|
| tables, e.g. for word vectors.
|
2017-10-27 18:07:26 +03:00
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+row
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+cell #[code cluster]
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+cell int
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+cell Brown cluster ID.
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+row
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+cell #[code _]
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+cell #[code Underscore]
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+cell
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| User space for adding custom
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| #[+a("/usage/processing-pipelines#custom-components-attributes") attribute extensions].
|