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60 lines
1.9 KiB
Cython
60 lines
1.9 KiB
Cython
# cython: profile=True
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# cython: embedsignature=True
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'''Tokenize English text, using a scheme that differs from the Penn Treebank 3
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scheme in several important respects:
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* Whitespace is added as tokens, except for single spaces. e.g.,
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>>> [w.string for w in EN.tokenize(u'\\nHello \\tThere')]
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[u'\\n', u'Hello', u' ', u'\\t', u'There']
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* Contractions are normalized, e.g.
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>>> [w.string for w in EN.tokenize(u"isn't ain't won't he's")]
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[u'is', u'not', u'are', u'not', u'will', u'not', u'he', u"__s"]
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* Hyphenated words are split, with the hyphen preserved, e.g.:
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>>> [w.string for w in EN.tokenize(u'New York-based')]
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[u'New', u'York', u'-', u'based']
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Other improvements:
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* Email addresses, URLs, European-formatted dates and other numeric entities not
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found in the PTB are tokenized correctly
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* Heuristic handling of word-final periods (PTB expects sentence boundary detection
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as a pre-process before tokenization.)
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Take care to ensure your training and run-time data is tokenized according to the
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same scheme. Tokenization problems are a major cause of poor performance for
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NLP tools. If you're using a pre-trained model, the :py:mod:`spacy.ptb3` module
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provides a fully Penn Treebank 3-compliant tokenizer.
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'''
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# TODO
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#The script translate_treebank_tokenization can be used to transform a treebank's
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#annotation to use one of the spacy tokenization schemes.
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from __future__ import unicode_literals
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from libc.stdint cimport uint64_t
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cimport lang
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from spacy.lexeme cimport lexeme_check_flag
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from spacy.lexeme cimport lexeme_string_view
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from spacy import orth
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cdef class English(Language):
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"""English tokenizer, tightly coupled to lexicon.
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Attributes:
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name (unicode): The two letter code used by Wikipedia for the language.
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lexicon (Lexicon): The lexicon. Exposes the lookup method.
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"""
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pass
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EN = English('en', [], [])
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