mirror of https://github.com/explosion/spaCy.git
269 lines
8.7 KiB
Cython
269 lines
8.7 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.stdlib cimport malloc, calloc, free
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from libc.stdint cimport uint64_t
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cimport lang
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from spacy import util
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from spacy import orth
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cdef enum Flags:
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Flag_IsAlpha
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Flag_IsAscii
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Flag_IsDigit
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Flag_IsLower
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Flag_IsPunct
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Flag_IsSpace
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Flag_IsTitle
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Flag_IsUpper
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Flag_CanAdj
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Flag_CanAdp
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Flag_CanAdv
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Flag_CanConj
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Flag_CanDet
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Flag_CanNoun
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Flag_CanNum
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Flag_CanPdt
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Flag_CanPos
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Flag_CanPron
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Flag_CanPrt
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Flag_CanPunct
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Flag_CanVerb
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Flag_OftLower
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Flag_OftTitle
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Flag_OftUpper
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Flag_N
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cdef enum Views:
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View_CanonForm
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View_WordShape
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View_NonSparse
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View_Asciied
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View_N
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# Assign the flag and view functions by enum value.
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# This is verbose, but it ensures we don't get nasty order sensitivities.
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STRING_VIEW_FUNCS = [None] * View_N
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STRING_VIEW_FUNCS[View_CanonForm] = orth.canon_case
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STRING_VIEW_FUNCS[View_WordShape] = orth.word_shape
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STRING_VIEW_FUNCS[View_NonSparse] = orth.non_sparse
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STRING_VIEW_FUNCS[View_Asciied] = orth.asciied
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FLAG_FUNCS = [None] * Flag_N
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FLAG_FUNCS[Flag_IsAlpha] = orth.is_alpha
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FLAG_FUNCS[Flag_IsAscii] = orth.is_ascii
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FLAG_FUNCS[Flag_IsDigit] = orth.is_digit
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FLAG_FUNCS[Flag_IsLower] = orth.is_lower
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FLAG_FUNCS[Flag_IsPunct] = orth.is_punct
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FLAG_FUNCS[Flag_IsSpace] = orth.is_space
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FLAG_FUNCS[Flag_IsTitle] = orth.is_title
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FLAG_FUNCS[Flag_IsUpper] = orth.is_upper
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FLAG_FUNCS[Flag_CanAdj] = orth.can_tag('ADJ')
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FLAG_FUNCS[Flag_CanAdp] = orth.can_tag('ADP')
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FLAG_FUNCS[Flag_CanAdv] = orth.can_tag('ADV')
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FLAG_FUNCS[Flag_CanConj] = orth.can_tag('CONJ')
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FLAG_FUNCS[Flag_CanDet] = orth.can_tag('DET')
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FLAG_FUNCS[Flag_CanNoun] = orth.can_tag('NOUN')
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FLAG_FUNCS[Flag_CanNum] = orth.can_tag('NUM')
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FLAG_FUNCS[Flag_CanPdt] = orth.can_tag('PDT')
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FLAG_FUNCS[Flag_CanPos] = orth.can_tag('POS')
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FLAG_FUNCS[Flag_CanPron] = orth.can_tag('PRON')
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FLAG_FUNCS[Flag_CanPrt] = orth.can_tag('PRT')
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FLAG_FUNCS[Flag_CanPunct] = orth.can_tag('PUNCT')
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FLAG_FUNCS[Flag_CanVerb] = orth.can_tag('VERB')
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FLAG_FUNCS[Flag_OftLower] = orth.oft_case('lower', 0.7)
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FLAG_FUNCS[Flag_OftTitle] = orth.oft_case('title', 0.7)
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FLAG_FUNCS[Flag_OftUpper] = orth.oft_case('upper', 0.7)
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cdef class EnglishTokens(Tokens):
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# Provide accessor methods for the features supported by the language.
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# Without these, clients have to use the underlying string_view and check_flag
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# methods, which requires them to know the IDs.
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cpdef unicode canon_string(self, size_t i):
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return self.lexemes[i].string_view(View_CanonForm)
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cpdef unicode shape_string(self, size_t i):
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return self.lexemes[i].string_view(View_WordShape)
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cpdef unicode non_sparse_string(self, size_t i):
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return self.lexemes[i].string_view(View_NonSparse)
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cpdef unicode asciied(self, size_t i):
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return self.lexemes[i].string_views(View_Asciied)
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cpdef bint is_alpha(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsAlpha)
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cpdef bint is_ascii(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsAscii)
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cpdef bint is_digit(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsDigit)
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cpdef bint is_lower(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsLower)
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cpdef bint is_punct(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsPunct)
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cpdef bint is_space(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsSpace)
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cpdef bint is_title(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsTitle)
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cpdef bint is_upper(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_IsUpper)
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cpdef bint can_adj(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanAdj)
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cpdef bint can_adp(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanAdp)
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cpdef bint can_adv(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanAdv)
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cpdef bint can_conj(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanConj)
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cpdef bint can_det(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanDet)
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cpdef bint can_noun(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanNoun)
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cpdef bint can_num(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanNum)
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cpdef bint can_pdt(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanPdt)
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cpdef bint can_pos(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanPos)
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cpdef bint can_pron(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanPron)
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cpdef bint can_prt(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanPrt)
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cpdef bint can_punct(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanPunct)
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cpdef bint can_verb(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_CanVerb)
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cpdef bint oft_lower(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_OftLower)
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cpdef bint oft_title(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_OftTitle)
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cpdef bint oft_upper(self, size_t i):
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return self.lexemes[i].check_flag(i, Flag_OftUpper)
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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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fl_is_alpha = Flag_IsAlpha
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fl_is_digit = Flag_IsDigit
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v_shape = View_WordShape
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def __cinit__(self, name, user_string_features, user_flag_features):
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self.cache = {}
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lang_data = util.read_lang_data(name)
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rules, words, probs, clusters, case_stats, tag_stats = lang_data
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self.lexicon = lang.Lexicon(words, probs, clusters, case_stats, tag_stats,
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STRING_VIEW_FUNCS + user_string_features,
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FLAG_FUNCS + user_flag_features)
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self._load_special_tokenization(rules)
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self.tokens_class = EnglishTokens
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cdef int _split_one(self, unicode word):
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cdef size_t length = len(word)
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cdef int i = 0
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if word.startswith("'s") or word.startswith("'S"):
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return 2
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# Contractions
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if word.endswith("'s") and length >= 3:
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return length - 2
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# Leading punctuation
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if _check_punct(word, 0, length):
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return 1
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elif length >= 1:
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# Split off all trailing punctuation characters
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i = 0
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while i < length and not _check_punct(word, i, length):
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i += 1
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return i
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cdef bint _check_punct(unicode word, size_t i, size_t length):
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# Don't count appostrophes as punct if the next char is a letter
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if word[i] == "'" and i < (length - 1) and word[i+1].isalpha():
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return i == 0
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if word[i] == "-" and i < (length - 1) and word[i+1] == '-':
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return False
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# Don't count commas as punct if the next char is a number
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if word[i] == "," and i < (length - 1) and word[i+1].isdigit():
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return False
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# Don't count periods as punct if the next char is not whitespace
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if word[i] == "." and i < (length - 1) and not word[i+1].isspace():
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return False
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return not word[i].isalnum()
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EN = English('en', [], [])
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