spaCy/spacy/en.pyx

269 lines
8.7 KiB
Cython

# cython: profile=True
# cython: embedsignature=True
'''Tokenize English text, using a scheme that differs from the Penn Treebank 3
scheme in several important respects:
* Whitespace is added as tokens, except for single spaces. e.g.,
>>> [w.string for w in EN.tokenize(u'\\nHello \\tThere')]
[u'\\n', u'Hello', u' ', u'\\t', u'There']
* Contractions are normalized, e.g.
>>> [w.string for w in EN.tokenize(u"isn't ain't won't he's")]
[u'is', u'not', u'are', u'not', u'will', u'not', u'he', u"__s"]
* Hyphenated words are split, with the hyphen preserved, e.g.:
>>> [w.string for w in EN.tokenize(u'New York-based')]
[u'New', u'York', u'-', u'based']
Other improvements:
* Email addresses, URLs, European-formatted dates and other numeric entities not
found in the PTB are tokenized correctly
* Heuristic handling of word-final periods (PTB expects sentence boundary detection
as a pre-process before tokenization.)
Take care to ensure your training and run-time data is tokenized according to the
same scheme. Tokenization problems are a major cause of poor performance for
NLP tools. If you're using a pre-trained model, the :py:mod:`spacy.ptb3` module
provides a fully Penn Treebank 3-compliant tokenizer.
'''
# TODO
#The script translate_treebank_tokenization can be used to transform a treebank's
#annotation to use one of the spacy tokenization schemes.
from __future__ import unicode_literals
from libc.stdlib cimport malloc, calloc, free
from libc.stdint cimport uint64_t
cimport lang
from spacy import util
from spacy import orth
cdef enum Flags:
Flag_IsAlpha
Flag_IsAscii
Flag_IsDigit
Flag_IsLower
Flag_IsPunct
Flag_IsSpace
Flag_IsTitle
Flag_IsUpper
Flag_CanAdj
Flag_CanAdp
Flag_CanAdv
Flag_CanConj
Flag_CanDet
Flag_CanNoun
Flag_CanNum
Flag_CanPdt
Flag_CanPos
Flag_CanPron
Flag_CanPrt
Flag_CanPunct
Flag_CanVerb
Flag_OftLower
Flag_OftTitle
Flag_OftUpper
Flag_N
cdef enum Views:
View_CanonForm
View_WordShape
View_NonSparse
View_Asciied
View_N
# Assign the flag and view functions by enum value.
# This is verbose, but it ensures we don't get nasty order sensitivities.
STRING_VIEW_FUNCS = [None] * View_N
STRING_VIEW_FUNCS[View_CanonForm] = orth.canon_case
STRING_VIEW_FUNCS[View_WordShape] = orth.word_shape
STRING_VIEW_FUNCS[View_NonSparse] = orth.non_sparse
STRING_VIEW_FUNCS[View_Asciied] = orth.asciied
FLAG_FUNCS = [None] * Flag_N
FLAG_FUNCS[Flag_IsAlpha] = orth.is_alpha
FLAG_FUNCS[Flag_IsAscii] = orth.is_ascii
FLAG_FUNCS[Flag_IsDigit] = orth.is_digit
FLAG_FUNCS[Flag_IsLower] = orth.is_lower
FLAG_FUNCS[Flag_IsPunct] = orth.is_punct
FLAG_FUNCS[Flag_IsSpace] = orth.is_space
FLAG_FUNCS[Flag_IsTitle] = orth.is_title
FLAG_FUNCS[Flag_IsUpper] = orth.is_upper
FLAG_FUNCS[Flag_CanAdj] = orth.can_tag('ADJ')
FLAG_FUNCS[Flag_CanAdp] = orth.can_tag('ADP')
FLAG_FUNCS[Flag_CanAdv] = orth.can_tag('ADV')
FLAG_FUNCS[Flag_CanConj] = orth.can_tag('CONJ')
FLAG_FUNCS[Flag_CanDet] = orth.can_tag('DET')
FLAG_FUNCS[Flag_CanNoun] = orth.can_tag('NOUN')
FLAG_FUNCS[Flag_CanNum] = orth.can_tag('NUM')
FLAG_FUNCS[Flag_CanPdt] = orth.can_tag('PDT')
FLAG_FUNCS[Flag_CanPos] = orth.can_tag('POS')
FLAG_FUNCS[Flag_CanPron] = orth.can_tag('PRON')
FLAG_FUNCS[Flag_CanPrt] = orth.can_tag('PRT')
FLAG_FUNCS[Flag_CanPunct] = orth.can_tag('PUNCT')
FLAG_FUNCS[Flag_CanVerb] = orth.can_tag('VERB')
FLAG_FUNCS[Flag_OftLower] = orth.oft_case('lower', 0.7)
FLAG_FUNCS[Flag_OftTitle] = orth.oft_case('title', 0.7)
FLAG_FUNCS[Flag_OftUpper] = orth.oft_case('upper', 0.7)
cdef class EnglishTokens(Tokens):
# Provide accessor methods for the features supported by the language.
# Without these, clients have to use the underlying string_view and check_flag
# methods, which requires them to know the IDs.
cpdef unicode canon_string(self, size_t i):
return self.lexemes[i].string_view(View_CanonForm)
cpdef unicode shape_string(self, size_t i):
return self.lexemes[i].string_view(View_WordShape)
cpdef unicode non_sparse_string(self, size_t i):
return self.lexemes[i].string_view(View_NonSparse)
cpdef unicode asciied(self, size_t i):
return self.lexemes[i].string_views(View_Asciied)
cpdef bint is_alpha(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsAlpha)
cpdef bint is_ascii(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsAscii)
cpdef bint is_digit(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsDigit)
cpdef bint is_lower(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsLower)
cpdef bint is_punct(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsPunct)
cpdef bint is_space(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsSpace)
cpdef bint is_title(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsTitle)
cpdef bint is_upper(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_IsUpper)
cpdef bint can_adj(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanAdj)
cpdef bint can_adp(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanAdp)
cpdef bint can_adv(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanAdv)
cpdef bint can_conj(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanConj)
cpdef bint can_det(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanDet)
cpdef bint can_noun(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanNoun)
cpdef bint can_num(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanNum)
cpdef bint can_pdt(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanPdt)
cpdef bint can_pos(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanPos)
cpdef bint can_pron(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanPron)
cpdef bint can_prt(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanPrt)
cpdef bint can_punct(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanPunct)
cpdef bint can_verb(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_CanVerb)
cpdef bint oft_lower(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_OftLower)
cpdef bint oft_title(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_OftTitle)
cpdef bint oft_upper(self, size_t i):
return self.lexemes[i].check_flag(i, Flag_OftUpper)
cdef class English(Language):
"""English tokenizer, tightly coupled to lexicon.
Attributes:
name (unicode): The two letter code used by Wikipedia for the language.
lexicon (Lexicon): The lexicon. Exposes the lookup method.
"""
fl_is_alpha = Flag_IsAlpha
fl_is_digit = Flag_IsDigit
v_shape = View_WordShape
def __cinit__(self, name, user_string_features, user_flag_features):
self.cache = {}
lang_data = util.read_lang_data(name)
rules, words, probs, clusters, case_stats, tag_stats = lang_data
self.lexicon = lang.Lexicon(words, probs, clusters, case_stats, tag_stats,
STRING_VIEW_FUNCS + user_string_features,
FLAG_FUNCS + user_flag_features)
self._load_special_tokenization(rules)
self.tokens_class = EnglishTokens
cdef int _split_one(self, unicode word):
cdef size_t length = len(word)
cdef int i = 0
if word.startswith("'s") or word.startswith("'S"):
return 2
# Contractions
if word.endswith("'s") and length >= 3:
return length - 2
# Leading punctuation
if _check_punct(word, 0, length):
return 1
elif length >= 1:
# Split off all trailing punctuation characters
i = 0
while i < length and not _check_punct(word, i, length):
i += 1
return i
cdef bint _check_punct(unicode word, size_t i, size_t length):
# Don't count appostrophes as punct if the next char is a letter
if word[i] == "'" and i < (length - 1) and word[i+1].isalpha():
return i == 0
if word[i] == "-" and i < (length - 1) and word[i+1] == '-':
return False
# Don't count commas as punct if the next char is a number
if word[i] == "," and i < (length - 1) and word[i+1].isdigit():
return False
# Don't count periods as punct if the next char is not whitespace
if word[i] == "." and i < (length - 1) and not word[i+1].isspace():
return False
return not word[i].isalnum()
EN = English('en', [], [])