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* Fix ptb3 module
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Matthew Honnibal
parent
a22101404a
commit
e289896603
14
spacy/en.pyx
14
spacy/en.pyx
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@ -3,7 +3,7 @@
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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 added as tokens, except for single spaces. e.g.,
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* Whitespace is added as tokens, except for single spaces. e.g.,
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>>> tokenize(u'\\nHello \\tThere').strings
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[u'\\n', u'Hello', u' ', u'\\t', u'There']
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@ -18,13 +18,15 @@ scheme in several important respects:
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>>> tokenize(u'New York-based').strings
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[u'New', u'York', u'-', u'based']
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Other improvements:
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* Full unicode support
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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 you training and run-time data is tokenized according to the
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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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@ -49,7 +51,6 @@ from .orthography.latin import *
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from .lexeme import *
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cdef class English(spacy.Language):
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# How to ensure the order here aligns with orthography.latin?
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view_funcs = [
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@ -101,7 +102,7 @@ cpdef Tokens tokenize(unicode string):
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The tokenization rules are defined in two places:
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* The data/en/tokenization table, which handles special cases like contractions;
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* The `spacy.en.English.find_split` function, which is used to split off punctuation etc.
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* The :py:meth:`spacy.en.English.find_split` function, which is used to split off punctuation etc.
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Args:
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string (unicode): The string to be tokenized.
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@ -113,9 +114,10 @@ cpdef Tokens tokenize(unicode string):
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cpdef LexID lookup(unicode string) except 0:
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"""Retrieve (or create, if not found) a Lexeme ID for a string.
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"""Retrieve (or create, if not found) a Lexeme for a string, and return its ID.
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The LexID is really a memory address, making dereferencing it essentially free.
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Properties of the Lexeme are accessed by passing LexID to the accessor methods.
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Access is cheap/free, as the LexID is the memory address of the Lexeme.
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Args:
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string (unicode): The string to be looked up. Must be unicode, not bytes.
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@ -25,10 +25,15 @@ cdef struct Lexeme:
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cpdef StringHash lex_of(LexID lex_id) except 0
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cpdef char first_of(LexID lex_id) except 0
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cpdef size_t length_of(LexID lex_id) except 0
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cpdef double prob_of(LexID lex_id) except 0
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cpdef double prob_of(LexID lex_id) except 1
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cpdef ClusterID cluster_of(LexID lex_id) except 0
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cpdef bint check_tag_flag(LexID lex, TagFlags flag) except *
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cpdef bint is_often_titled(size_t lex_id)
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cpdef bint is_often_uppered(size_t lex_id)
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cpdef bint can_tag(LexID lex, TagFlags flag) except *
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cpdef bint check_dist_flag(LexID lex, DistFlags flag) except *
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cpdef bint check_orth_flag(LexID lex, OrthFlags flag) except *
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@ -11,6 +11,21 @@ from libc.stdint cimport uint64_t
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from spacy.spacy cimport StringHash
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# Python-visible enum for POS tags
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PUNCT = 0
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CONJ = 1
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NUM = 2
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X = 3
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DET = 4
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ADP = 5
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ADJ = 6
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ADV = 7
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VERB = 8
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NOUN = 9
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PDT = 10
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POS = 11
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PRON = 12
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PRT = 13
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cpdef int set_flags(LexID lex_id, object active_flags) except *:
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"""Set orthographic bit flags for a Lexeme.
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@ -75,7 +90,7 @@ cpdef size_t length_of(size_t lex_id) except 0:
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return word.length
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cpdef double prob_of(size_t lex_id) except 0:
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cpdef double prob_of(size_t lex_id) except 1:
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'''Access an estimate of the word's unigram log probability.
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Probabilities are calculated from a large text corpus, and smoothed using
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@ -90,7 +105,7 @@ cpdef double prob_of(size_t lex_id) except 0:
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DEF OFT_UPPER = 1
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DEF OFT_TITLE = 2
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cpdef bint is_oft_upper(size_t lex_id):
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cpdef bint is_often_uppered(size_t lex_id):
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'''Check the OFT_UPPER distributional flag for the word.
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The OFT_UPPER flag records whether a lower-cased version of the word
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@ -101,15 +116,15 @@ cpdef bint is_oft_upper(size_t lex_id):
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Case statistics are estimated from a large text corpus. Estimates are read
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from data/en/case_stats, and can be replaced using spacy.en.load_case_stats.
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>>> is_oft_upper(lookup(u'nato'))
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>>> is_often_uppered(lookup(u'nato'))
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True
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>>> is_oft_upper(lookup(u'the'))
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>>> is_often_uppered(lookup(u'the'))
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False
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'''
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return (<Lexeme*>lex_id).dist_flags & (1 << OFT_UPPER)
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cpdef bint is_oft_title(size_t lex_id):
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cpdef bint is_often_titled(size_t lex_id):
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'''Check the OFT_TITLE distributional flag for the word.
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The OFT_TITLE flag records whether a lower-cased version of the word
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@ -127,6 +142,7 @@ cpdef bint is_oft_title(size_t lex_id):
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'''
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return (<Lexeme*>lex_id).dist_flags & (1 << OFT_TITLE)
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cpdef bint check_orth_flag(size_t lex_id, OrthFlags flag) except *:
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return (<Lexeme*>lex_id).orth_flags & (1 << flag)
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@ -135,5 +151,5 @@ cpdef bint check_dist_flag(size_t lex_id, DistFlags flag) except *:
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return (<Lexeme*>lex_id).dist_flags & (1 << flag)
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cpdef bint check_tag_flag(LexID lex_id, TagFlags flag) except *:
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cpdef bint can_tag(LexID lex_id, TagFlags flag) except *:
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return (<Lexeme*>lex_id).possible_tags & (1 << flag)
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@ -1,18 +1,15 @@
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from libcpp.vector cimport vector
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from spacy.spacy cimport StringHash
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from spacy.spacy cimport Language
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from spacy.spacy cimport Lexeme
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from spacy.spacy cimport Lexeme_addr
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from spacy.lexeme cimport LexID
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from spacy.tokens cimport Tokens
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from spacy.lexeme cimport StringHash
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cdef class EnglishPTB(Language):
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cdef int find_split(self, unicode word)
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cdef class PennTreebank3(Language):
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cpdef list find_substrings(self, unicode word)
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cdef EnglishPTB EN_PTB
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cdef PennTreebank3 PTB3
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cpdef Lexeme_addr lookup(unicode word) except 0
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cpdef LexID lookup(unicode word) except 0
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cpdef Tokens tokenize(unicode string)
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cpdef unicode unhash(StringHash hash_value)
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108
spacy/ptb3.pyx
108
spacy/ptb3.pyx
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@ -7,55 +7,89 @@ 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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from libcpp.vector cimport vector
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from spacy.string_tools cimport substr
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from spacy.spacy cimport Language
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from . import util
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cimport spacy
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import re
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cdef class EnglishPTB(Language):
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cdef int find_split(self, unicode word):
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length = len(word)
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cdef int i = 0
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# Contractions
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if word.endswith("'s"):
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return length - 2
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# Leading punctuation
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if is_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 is_punct(word, i, length):
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i += 1
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return i
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# List of contractions adapted from Robert MacIntyre's tokenizer.
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CONTRACTIONS2 = [re.compile(r"(?i)\b(can)(not)\b"),
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re.compile(r"(?i)\b(d)('ye)\b"),
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re.compile(r"(?i)\b(gim)(me)\b"),
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re.compile(r"(?i)\b(gon)(na)\b"),
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re.compile(r"(?i)\b(got)(ta)\b"),
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re.compile(r"(?i)\b(lem)(me)\b"),
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re.compile(r"(?i)\b(mor)('n)\b"),
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re.compile(r"(?i)\b(wan)(na) ")]
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CONTRACTIONS3 = [re.compile(r"(?i) ('t)(is)\b"),
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re.compile(r"(?i) ('t)(was)\b")]
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CONTRACTIONS4 = [re.compile(r"(?i)\b(whad)(dd)(ya)\b"),
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re.compile(r"(?i)\b(wha)(t)(cha)\b")]
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def nltk_regex_tokenize(text):
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# Implementation taken from NLTK 3.0, based on tokenizer.sed
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#starting quotes
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text = re.sub(r'^\"', r'``', text)
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text = re.sub(r'(``)', r' \1 ', text)
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text = re.sub(r'([ (\[{<])"', r'\1 `` ', text)
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#punctuation
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text = re.sub(r'([:,])([^\d])', r' \1 \2', text)
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text = re.sub(r'\.\.\.', r' ... ', text)
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text = re.sub(r'[;@#$%&]', r' \g<0> ', text)
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text = re.sub(r'([^\.])(\.)([\]\)}>"\']*)\s*$', r'\1 \2\3 ', text)
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text = re.sub(r'[?!]', r' \g<0> ', text)
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text = re.sub(r"([^'])' ", r"\1 ' ", text)
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#parens, brackets, etc.
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text = re.sub(r'[\]\[\(\)\{\}\<\>]', r' \g<0> ', text)
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text = re.sub(r'--', r' -- ', text)
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#add extra space to make things easier
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text = " " + text + " "
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#ending quotes
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text = re.sub(r'"', " '' ", text)
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text = re.sub(r'(\S)(\'\')', r'\1 \2 ', text)
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text = re.sub(r"([^' ])('[sS]|'[mM]|'[dD]|') ", r"\1 \2 ", text)
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text = re.sub(r"([^' ])('ll|'LL|'re|'RE|'ve|'VE|n't|N'T) ", r"\1 \2 ",
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text)
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for regexp in CONTRACTIONS2:
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text = regexp.sub(r' \1 \2 ', text)
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for regexp in CONTRACTIONS3:
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text = regexp.sub(r' \1 \2 ', text)
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# We are not using CONTRACTIONS4 since
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# they are also commented out in the SED scripts
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# for regexp in self.CONTRACTIONS4:
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# text = regexp.sub(r' \1 \2 \3 ', text)
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return text.split()
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cdef bint is_punct(unicode word, size_t i, size_t length):
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is_final = i == (length - 1)
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if word[i] == '.':
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return False
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if not is_final and word[i] == '-' and word[i+1] == '-':
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return True
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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 False
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punct_chars = set(',;:' + '@#$%&' + '!?' + '[({' + '})]')
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return word[i] in punct_chars
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cdef class PennTreebank3(Language):
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cpdef list find_substrings(self, unicode chunk):
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strings = nltk_regex_tokenize(chunk)
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assert strings
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return strings
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cdef EnglishPTB EN_PTB = EnglishPTB('en_ptb')
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cdef PennTreebank3 PTB3 = PennTreebank3('ptb3')
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cpdef Tokens tokenize(unicode string):
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return EN_PTB.tokenize(string)
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return PTB3.tokenize(string)
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cpdef Lexeme_addr lookup(unicode string) except 0:
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return <Lexeme_addr>EN_PTB.lookup(string)
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cpdef LexID lookup(unicode string) except 0:
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return <LexID>PTB3.lookup(string)
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cpdef unicode unhash(StringHash hash_value):
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return EN_PTB.unhash(hash_value)
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return PTB3.unhash(hash_value)
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