mirror of https://github.com/explosion/spaCy.git
50 lines
1.5 KiB
ReStructuredText
50 lines
1.5 KiB
ReStructuredText
Lexical Lookup
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Where possible, spaCy computes information over lexical *types*, rather than
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*tokens*. If you process a large batch of text, the number of unique types
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you will see will grow exponentially slower than the number of tokens --- so
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it's much more efficient to compute over types. And, in small samples, we generally
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want to know about the distribution of a word in the language at large ---
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which again, is type-based information.
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You can access the lexical features via the Token object, but you can also look them
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up in the vocabulary directly:
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>>> from spacy.en import English
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>>> nlp = English()
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>>> lexeme = nlp.vocab[u'Amazon']
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.. py:class:: vocab.Vocab(self, data_dir=None, lex_props_getter=None)
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.. py:method:: __len__(self) --> int
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.. py:method:: __getitem__(self, id: int) --> unicode
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.. py:method:: __getitem__(self, string: unicode) --> int
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.. py:method:: __setitem__(self, py_str: unicode, props: Dict[str, int[float]) --> None
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.. py:method:: dump(self, loc: unicode) --> None
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.. py:method:: load_lexemes(self, loc: unicode) --> None
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.. py:method:: load_vectors(self, loc: unicode) --> None
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.. py:class:: strings.StringStore(self)
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.. py:method:: __len__(self) --> int
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.. py:method:: __getitem__(self, id: int) --> unicode
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.. py:method:: __getitem__(self, string: bytes) --> id
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.. py:method:: __getitem__(self, string: unicode) --> id
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.. py:method:: dump(self, loc: unicode) --> None
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.. py:method:: load(self, loc: unicode) --> None
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