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spaCy/website/api/lexeme.jade
ines a8e10f94e4 Tidy up Lexeme and update docs
2017-10-27 21:07:50 +02:00

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//- 💫 DOCS > API > LEXEME
include ../_includes/_mixins
p
| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
| a word type, as opposed to a word token. It therefore has no
| part-of-speech tag, dependency parse, or lemma (if lemmatization depends
| on the part-of-speech tag).
+h(2, "init") Lexeme.__init__
+tag method
p Create a #[code Lexeme] object.
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell The parent vocabulary.
+row
+cell #[code orth]
+cell int
+cell The orth id of the lexeme.
+row("foot")
+cell returns
+cell #[code Lexeme]
+cell The newly constructed object.
+h(2, "set_flag") Lexeme.set_flag
+tag method
p Change the value of a boolean flag.
+aside-code("Example").
COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
+table(["Name", "Type", "Description"])
+row
+cell #[code flag_id]
+cell int
+cell The attribute ID of the flag to set.
+row
+cell #[code value]
+cell bool
+cell The new value of the flag.
+h(2, "check_flag") Lexeme.check_flag
+tag method
p Check the value of a boolean flag.
+aside-code("Example").
is_my_library = lambda text: text in ['spaCy', 'Thinc']
MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
+table(["Name", "Type", "Description"])
+row
+cell #[code flag_id]
+cell int
+cell The attribute ID of the flag to query.
+row("foot")
+cell returns
+cell bool
+cell The value of the flag.
+h(2, "similarity") Lexeme.similarity
+tag method
+tag-model("vectors")
p Compute a semantic similarity estimate. Defaults to cosine over vectors.
+aside-code("Example").
apple = nlp.vocab[u'apple']
orange = nlp.vocab[u'orange']
apple_orange = apple.similarity(orange)
orange_apple = orange.similarity(apple)
assert apple_orange == orange_apple
+table(["Name", "Type", "Description"])
+row
+cell other
+cell -
+cell
| The object to compare with. By default, accepts #[code Doc],
| #[code Span], #[code Token] and #[code Lexeme] objects.
+row("foot")
+cell returns
+cell float
+cell A scalar similarity score. Higher is more similar.
+h(2, "has_vector") Lexeme.has_vector
+tag property
+tag-model("vectors")
p
| A boolean value indicating whether a word vector is associated with the
| lexeme.
+aside-code("Example").
apple = nlp.vocab[u'apple']
assert apple.has_vector
+table(["Name", "Type", "Description"])
+row("foot")
+cell returns
+cell bool
+cell Whether the lexeme has a vector data attached.
+h(2, "vector") Lexeme.vector
+tag property
+tag-model("vectors")
p A real-valued meaning representation.
+aside-code("Example").
apple = nlp.vocab[u'apple']
assert apple.vector.dtype == 'float32'
assert apple.vector.shape == (300,)
+table(["Name", "Type", "Description"])
+row("foot")
+cell returns
+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
+cell A 1D numpy array representing the lexeme's semantics.
+h(2, "vector_norm") Lexeme.vector_norm
+tag property
+tag-model("vectors")
p The L2 norm of the lexeme's vector representation.
+aside-code("Example").
apple = nlp.vocab[u'apple']
pasta = nlp.vocab[u'pasta']
apple.vector_norm # 7.1346845626831055
pasta.vector_norm # 7.759851932525635
assert apple.vector_norm != pasta.vector_norm
+table(["Name", "Type", "Description"])
+row("foot")
+cell returns
+cell float
+cell The L2 norm of the vector representation.
+h(2, "attributes") Attributes
+table(["Name", "Type", "Description"])
+row
+cell #[code vocab]
+cell #[code Vocab]
+cell The lexeme's vocabulary.
+row
+cell #[code text]
+cell unicode
+cell Verbatim text content.
+row
+cell #[code orth]
+cell int
+cell ID of the verbatim text content.
+row
+cell #[code orth_]
+cell unicode
+cell
| Verbatim text content (identical to #[code Lexeme.text]). Existst
| mostly for consistency with the other attributes.
+row
+cell #[code lex_id]
+cell int
+cell ID of the lexeme's lexical type.
+row
+cell #[code rank]
+cell int
+cell
| Sequential ID of the lexemes's lexical type, used to index into
| tables, e.g. for word vectors.
+row
+cell #[code flags]
+cell int
+cell Container of the lexeme's binary flags.
+row
+cell #[code norm]
+cell int
+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
+row
+cell #[code norm_]
+cell unicode
+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
+row
+cell #[code lower]
+cell int
+cell Lowercase form of the word.
+row
+cell #[code lower_]
+cell unicode
+cell Lowercase form of the word.
+row
+cell #[code shape]
+cell int
+cell Transform of the word's string, to show orthographic features.
+row
+cell #[code shape_]
+cell unicode
+cell Transform of the word's string, to show orthographic features.
+row
+cell #[code prefix]
+cell int
+cell
| Length-N substring from the start of the word. Defaults to
| #[code N=1].
+row
+cell #[code prefix_]
+cell unicode
+cell
| Length-N substring from the start of the word. Defaults to
| #[code N=1].
+row
+cell #[code suffix]
+cell int
+cell
| Length-N substring from the end of the word. Defaults to
| #[code N=3].
+row
+cell #[code suffix_]
+cell unicode
+cell
| Length-N substring from the start of the word. Defaults to
| #[code N=3].
+row
+cell #[code is_alpha]
+cell bool
+cell
| Does the lexeme consist of alphabetic characters? Equivalent to
| #[code lexeme.text.isalpha()].
+row
+cell #[code is_ascii]
+cell bool
+cell
| Does the lexeme consist of ASCII characters? Equivalent to
| #[code [any(ord(c) >= 128 for c in lexeme.text)]].
+row
+cell #[code is_digit]
+cell bool
+cell
| Does the lexeme consist of digits? Equivalent to
| #[code lexeme.text.isdigit()].
+row
+cell #[code is_lower]
+cell bool
+cell
| Is the lexeme in lowercase? Equivalent to
| #[code lexeme.text.islower()].
+row
+cell #[code is_upper]
+cell bool
+cell
| Is the lexeme in uppercase? Equivalent to
| #[code lexeme.text.isupper()].
+row
+cell #[code is_title]
+cell bool
+cell
| Is the lexeme in titlecase? Equivalent to
| #[code lexeme.text.istitle()].
+row
+cell #[code is_punct]
+cell bool
+cell Is the lexeme punctuation?
+row
+cell #[code is_left_punct]
+cell bool
+cell Is the lexeme a left punctuation mark, e.g. #[code (]?
+row
+cell #[code is_right_punct]
+cell bool
+cell Is the lexeme a right punctuation mark, e.g. #[code )]?
+row
+cell #[code is_space]
+cell bool
+cell
| Does the lexeme consist of whitespace characters? Equivalent to
| #[code lexeme.text.isspace()].
+row
+cell #[code is_bracket]
+cell bool
+cell Is the lexeme a bracket?
+row
+cell #[code is_quote]
+cell bool
+cell Is the lexeme a quotation mark?
+row
+cell #[code like_url]
+cell bool
+cell Does the lexeme resemble a URL?
+row
+cell #[code like_num]
+cell bool
+cell Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.
+row
+cell #[code like_email]
+cell bool
+cell Does the lexeme resemble an email address?
+row
+cell #[code is_oov]
+cell bool
+cell Is the lexeme out-of-vocabulary?
+row
+cell #[code is_stop]
+cell bool
+cell Is the lexeme part of a "stop list"?
+row
+cell #[code lang]
+cell int
+cell Language of the parent vocabulary.
+row
+cell #[code lang_]
+cell unicode
+cell Language of the parent vocabulary.
+row
+cell #[code prob]
+cell float
+cell Smoothed log probability estimate of the lexeme's type.
+row
+cell #[code cluster]
+cell int
+cell Brown cluster ID.
+row
+cell #[code sentiment]
+cell float
+cell
| A scalar value indicating the positivity or negativity of the
| lexeme.
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