Resolve stopwords conflict to merge Dutch

This commit is contained in:
Ines Montani 2016-12-17 13:08:16 +01:00
commit f2c48ef504
14 changed files with 497 additions and 18 deletions

107
.github/contributors/RvanNieuwpoort.md vendored Executable file
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@ -0,0 +1,107 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
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**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
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example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
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2. With respect to any worldwide copyrights, or copyright applications and
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* you hereby assign to us joint ownership, and to the extent that such
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actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------------------- |
| Name | Rob van Nieuwpoort |
| Signing on behalf of | Dafne van Kuppevelt, Janneke van der Zwaan, Willem van Hage |
| Company name (if applicable) | Netherlands eScience center |
| Title or role (if applicable) | Director of technology |
| Date | 14-12-2016 |
| GitHub username | RvanNieuwpoort |
| Website (optional) | https://www.esciencecenter.nl/ |

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@ -6,10 +6,12 @@ This is a list of everyone who has made significant contributions to spaCy, in a
* Andreas Grivas, [@andreasgrv](https://github.com/andreasgrv) * Andreas Grivas, [@andreasgrv](https://github.com/andreasgrv)
* Chris DuBois, [@chrisdubois](https://github.com/chrisdubois) * Chris DuBois, [@chrisdubois](https://github.com/chrisdubois)
* Christoph Schwienheer, [@chssch](https://github.com/chssch) * Christoph Schwienheer, [@chssch](https://github.com/chssch)
* Dafne van Kuppevelt, [@dafnevk](https://github.com/dafnevk)
* Dmytro Sadovnychyi, [@sadovnychyi](https://github.com/sadovnychyi) * Dmytro Sadovnychyi, [@sadovnychyi](https://github.com/sadovnychyi)
* Henning Peters, [@henningpeters](https://github.com/henningpeters) * Henning Peters, [@henningpeters](https://github.com/henningpeters)
* Ines Montani, [@ines](https://github.com/ines) * Ines Montani, [@ines](https://github.com/ines)
* J Nicolas Schrading, [@NSchrading](https://github.com/NSchrading) * J Nicolas Schrading, [@NSchrading](https://github.com/NSchrading)
* Janneke van der Zwaan, [@jvdzwaan](https://github.com/jvdzwaan)
* Jordan Suchow, [@suchow](https://github.com/suchow) * Jordan Suchow, [@suchow](https://github.com/suchow)
* Kendrick Tan, [@kendricktan](https://github.com/kendricktan) * Kendrick Tan, [@kendricktan](https://github.com/kendricktan)
* Kyle P. Johnson, [@kylepjohnson](https://github.com/kylepjohnson) * Kyle P. Johnson, [@kylepjohnson](https://github.com/kylepjohnson)
@ -19,11 +21,13 @@ This is a list of everyone who has made significant contributions to spaCy, in a
* Maxim Samsonov, [@maxirmx](https://github.com/maxirmx) * Maxim Samsonov, [@maxirmx](https://github.com/maxirmx)
* Oleg Zd, [@olegzd](https://github.com/olegzd) * Oleg Zd, [@olegzd](https://github.com/olegzd)
* Pokey Rule, [@pokey](https://github.com/pokey) * Pokey Rule, [@pokey](https://github.com/pokey)
* Rob van Nieuwpoort, [@RvanNieuwpoort](https://github.com/RvanNieuwpoort)
* Sam Bozek, [@sambozek](https://github.com/sambozek) * Sam Bozek, [@sambozek](https://github.com/sambozek)
* Sasho Savkov [@savkov](https://github.com/savkov) * Sasho Savkov [@savkov](https://github.com/savkov)
* Tiago Rodrigues, [@TiagoMRodrigues](https://github.com/TiagoMRodrigues) * Tiago Rodrigues, [@TiagoMRodrigues](https://github.com/TiagoMRodrigues)
* Vsevolod Solovyov, [@vsolovyov](https://github.com/vsolovyov) * Vsevolod Solovyov, [@vsolovyov](https://github.com/vsolovyov)
* Wah Loon Keng, [@kengz](https://github.com/kengz) * Wah Loon Keng, [@kengz](https://github.com/kengz)
* Willem van Hage, [@wrvhage](https://github.com/wrvhage)
* Wolfgang Seeker, [@wbwseeker](https://github.com/wbwseeker) * Wolfgang Seeker, [@wbwseeker](https://github.com/wbwseeker)
* Yanhao Yang, [@YanhaoYang](https://github.com/YanhaoYang) * Yanhao Yang, [@YanhaoYang](https://github.com/YanhaoYang)
* Yubing Dong, [@tomtung](https://github.com/tomtung) * Yubing Dong, [@tomtung](https://github.com/tomtung)

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@ -151,10 +151,10 @@ def _read_senses(loc):
def setup_vocab(lex_attr_getters, tag_map, src_dir, dst_dir): def setup_vocab(lex_attr_getters, tag_map, src_dir, dst_dir):
if not dst_dir.exists(): if not dst_dir.exists():
dst_dir.mkdir() dst_dir.mkdir()
print('Reading vocab from ', src_dir)
vectors_src = src_dir / 'vectors.bz2' vectors_src = src_dir / 'vectors.bz2'
if vectors_src.exists(): if vectors_src.exists():
write_binary_vectors(vectors_src.as_posix, (dst_dir / 'vec.bin').as_posix()) write_binary_vectors(vectors_src.as_posix(), (dst_dir / 'vec.bin').as_posix())
else: else:
print("Warning: Word vectors file not found") print("Warning: Word vectors file not found")
vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=tag_map) vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=tag_map)

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@ -0,0 +1,22 @@
# Load NER
from __future__ import unicode_literals
import spacy
import pathlib
from spacy.pipeline import EntityRecognizer
from spacy.vocab import Vocab
def load_model(model_dir):
model_dir = pathlib.Path(model_dir)
nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
with (model_dir / 'vocab' / 'strings.json').open('r', encoding='utf8') as file_:
nlp.vocab.strings.load(file_)
nlp.vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
ner = EntityRecognizer.load(model_dir, nlp.vocab, require=True)
return (nlp, ner)
(nlp, ner) = load_model('ner')
doc = nlp.make_doc('Who is Shaka Khan?')
nlp.tagger(doc)
ner(doc)
for word in doc:
print(word.text, word.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)

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@ -10,6 +10,13 @@ from spacy.tagger import Tagger
def train_ner(nlp, train_data, entity_types): def train_ner(nlp, train_data, entity_types):
# Add new words to vocab.
for raw_text, _ in train_data:
doc = nlp.make_doc(raw_text)
for word in doc:
_ = nlp.vocab[word.orth]
# Train NER.
ner = EntityRecognizer(nlp.vocab, entity_types=entity_types) ner = EntityRecognizer(nlp.vocab, entity_types=entity_types)
for itn in range(5): for itn in range(5):
random.shuffle(train_data) random.shuffle(train_data)
@ -20,21 +27,30 @@ def train_ner(nlp, train_data, entity_types):
ner.model.end_training() ner.model.end_training()
return ner return ner
def save_model(ner, model_dir):
model_dir = pathlib.Path(model_dir)
if not model_dir.exists():
model_dir.mkdir()
assert model_dir.is_dir()
with (model_dir / 'config.json').open('w') as file_:
json.dump(ner.cfg, file_)
ner.model.dump(str(model_dir / 'model'))
if not (model_dir / 'vocab').exists():
(model_dir / 'vocab').mkdir()
ner.vocab.dump(str(model_dir / 'vocab' / 'lexemes.bin'))
with (model_dir / 'vocab' / 'strings.json').open('w', encoding='utf8') as file_:
ner.vocab.strings.dump(file_)
def main(model_dir=None): def main(model_dir=None):
if model_dir is not None:
model_dir = pathlib.Path(model_dir)
if not model_dir.exists():
model_dir.mkdir()
assert model_dir.is_dir()
nlp = spacy.load('en', parser=False, entity=False, add_vectors=False) nlp = spacy.load('en', parser=False, entity=False, add_vectors=False)
# v1.1.2 onwards # v1.1.2 onwards
if nlp.tagger is None: if nlp.tagger is None:
print('---- WARNING ----') print('---- WARNING ----')
print('Data directory not found') print('Data directory not found')
print('please run: `python -m spacy.en.download force all` for better performance') print('please run: `python -m spacy.en.download --force all` for better performance')
print('Using feature templates for tagging') print('Using feature templates for tagging')
print('-----------------') print('-----------------')
nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates) nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates)
@ -56,16 +72,17 @@ def main(model_dir=None):
nlp.tagger(doc) nlp.tagger(doc)
ner(doc) ner(doc)
for word in doc: for word in doc:
print(word.text, word.tag_, word.ent_type_, word.ent_iob) print(word.text, word.orth, word.lower, word.tag_, word.ent_type_, word.ent_iob)
if model_dir is not None: if model_dir is not None:
with (model_dir / 'config.json').open('w') as file_: save_model(ner, model_dir)
json.dump(ner.cfg, file_)
ner.model.dump(str(model_dir / 'model'))
if __name__ == '__main__': if __name__ == '__main__':
main() main('ner')
# Who "" 2 # Who "" 2
# is "" 2 # is "" 2
# Shaka "" PERSON 3 # Shaka "" PERSON 3

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@ -69,7 +69,7 @@ def main(output_dir=None):
print(word.text, word.tag_, word.pos_) print(word.text, word.tag_, word.pos_)
if output_dir is not None: if output_dir is not None:
tagger.model.dump(str(output_dir / 'pos' / 'model')) tagger.model.dump(str(output_dir / 'pos' / 'model'))
with (output_dir / 'vocab' / 'strings.json').open('wb') as file_: with (output_dir / 'vocab' / 'strings.json').open('w') as file_:
tagger.vocab.strings.dump(file_) tagger.vocab.strings.dump(file_)

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@ -28,6 +28,7 @@ PACKAGES = [
'spacy.fr', 'spacy.fr',
'spacy.it', 'spacy.it',
'spacy.pt', 'spacy.pt',
'spacy.nl',
'spacy.serialize', 'spacy.serialize',
'spacy.syntax', 'spacy.syntax',
'spacy.munge', 'spacy.munge',

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@ -10,6 +10,7 @@ from . import es
from . import it from . import it
from . import fr from . import fr
from . import pt from . import pt
from . import nl
try: try:
@ -25,6 +26,7 @@ set_lang_class(pt.Portuguese.lang, pt.Portuguese)
set_lang_class(fr.French.lang, fr.French) set_lang_class(fr.French.lang, fr.French)
set_lang_class(it.Italian.lang, it.Italian) set_lang_class(it.Italian.lang, it.Italian)
set_lang_class(zh.Chinese.lang, zh.Chinese) set_lang_class(zh.Chinese.lang, zh.Chinese)
set_lang_class(nl.Dutch.lang, nl.Dutch)
def load(name, **overrides): def load(name, **overrides):

26
spacy/nl/__init__.py Normal file
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@ -0,0 +1,26 @@
from __future__ import unicode_literals, print_function
from os import path
from ..language import Language
from ..attrs import LANG
from . import language_data
class Dutch(Language):
lang = 'nl'
class Defaults(Language.Defaults):
tokenizer_exceptions = dict(language_data.TOKENIZER_EXCEPTIONS)
lex_attr_getters = dict(Language.Defaults.lex_attr_getters)
lex_attr_getters[LANG] = lambda text: 'nl'
prefixes = tuple(language_data.TOKENIZER_PREFIXES)
suffixes = tuple(language_data.TOKENIZER_SUFFIXES)
infixes = tuple(language_data.TOKENIZER_INFIXES)
tag_map = dict(language_data.TAG_MAP)
stop_words = set(language_data.STOP_WORDS)

285
spacy/nl/language_data.py Normal file
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@ -0,0 +1,285 @@
# encoding: utf8
from __future__ import unicode_literals
import re
# Stop words are retrieved from http://www.damienvanholten.com/downloads/dutch-stop-words.txt
STOP_WORDS = set("""
aan
af
al
alles
als
altijd
andere
ben
bij
daar
dan
dat
de
der
deze
die
dit
doch
doen
door
dus
een
eens
en
er
ge
geen
geweest
haar
had
heb
hebben
heeft
hem
het
hier
hij
hoe
hun
iemand
iets
ik
in
is
ja
je
kan
kon
kunnen
maar
me
meer
men
met
mij
mijn
moet
na
naar
niet
niets
nog
nu
of
om
omdat
ons
ook
op
over
reeds
te
tegen
toch
toen
tot
u
uit
uw
van
veel
voor
want
waren
was
wat
we
wel
werd
wezen
wie
wij
wil
worden
zal
ze
zei
zelf
zich
zij
zijn
zo
zonder
zou
""".split())
TOKENIZER_PREFIXES = map(re.escape, r'''
,
"
(
[
{
*
<
>
$
£
'
``
`
#
US$
C$
A$
a-
....
...
»
_
§
'''.strip().split('\n'))
TOKENIZER_SUFFIXES = r'''
,
\"
\)
\]
\}
\*
\!
\?
%
\$
>
:
;
'
«
_
''
's
'S
s
S
°
\.\.
\.\.\.
\.\.\.\.
(?<=[a-zäöüßÖÄÜ)\]"'´«‘’%\)²“”])\.
\-\-
´
(?<=[0-9])km²
(?<=[0-9])
(?<=[0-9])cm²
(?<=[0-9])mm²
(?<=[0-9])km³
(?<=[0-9])
(?<=[0-9])cm³
(?<=[0-9])mm³
(?<=[0-9])ha
(?<=[0-9])km
(?<=[0-9])m
(?<=[0-9])cm
(?<=[0-9])mm
(?<=[0-9])µm
(?<=[0-9])nm
(?<=[0-9])yd
(?<=[0-9])in
(?<=[0-9])ft
(?<=[0-9])kg
(?<=[0-9])g
(?<=[0-9])mg
(?<=[0-9])µg
(?<=[0-9])t
(?<=[0-9])lb
(?<=[0-9])oz
(?<=[0-9])m/s
(?<=[0-9])km/h
(?<=[0-9])mph
(?<=[0-9])°C
(?<=[0-9])°K
(?<=[0-9])°F
(?<=[0-9])hPa
(?<=[0-9])Pa
(?<=[0-9])mbar
(?<=[0-9])mb
(?<=[0-9])T
(?<=[0-9])G
(?<=[0-9])M
(?<=[0-9])K
(?<=[0-9])kb
'''.strip().split('\n')
TOKENIZER_INFIXES = r'''
\.\.\.
(?<=[a-z])\.(?=[A-Z])
(?<=[a-zöäüßA-ZÖÄÜ"]):(?=[a-zöäüßA-ZÖÄÜ])
(?<=[a-zöäüßA-ZÖÄÜ"])>(?=[a-zöäüßA-ZÖÄÜ])
(?<=[a-zöäüßA-ZÖÄÜ"])<(?=[a-zöäüßA-ZÖÄÜ])
(?<=[a-zöäüßA-ZÖÄÜ"])=(?=[a-zöäüßA-ZÖÄÜ])
'''.strip().split('\n')
#TODO Make tokenizer excpetions for Dutch
TOKENIZER_EXCEPTIONS = {}
#TODO insert TAG_MAP for Dutch
TAG_MAP = {
"ADV": {
"pos": "ADV"
},
"NOUN": {
"pos": "NOUN"
},
"ADP": {
"pos": "ADP"
},
"PRON": {
"pos": "PRON"
},
"SCONJ": {
"pos": "SCONJ"
},
"PROPN": {
"pos": "PROPN"
},
"DET": {
"pos": "DET"
},
"SYM": {
"pos": "SYM"
},
"INTJ": {
"pos": "INTJ"
},
"PUNCT": {
"pos": "PUNCT"
},
"NUM": {
"pos": "NUM"
},
"AUX": {
"pos": "AUX"
},
"X": {
"pos": "X"
},
"CONJ": {
"pos": "CONJ"
},
"ADJ": {
"pos": "ADJ"
},
"VERB": {
"pos": "VERB"
}
}

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@ -426,3 +426,9 @@ cpdef enum symbol_t:
#IS_QUOTE #IS_QUOTE
#IS_LEFT_PUNCT #IS_LEFT_PUNCT
#IS_RIGHT_PUNCT #IS_RIGHT_PUNCT
# These symbols are currently missing. However, if we add them currently,
# we'll throw off the integer index and the model will have to be retrained.
# We therefore wait until the next data version to add them.
# acl

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@ -1,6 +1,7 @@
# encoding: utf8 # encoding: utf8
from __future__ import unicode_literals from __future__ import unicode_literals
from ...fr import French from ...fr import French
from ...nl import Dutch
def test_load_french(): def test_load_french():
nlp = French() nlp = French()
@ -10,3 +11,11 @@ def test_load_french():
assert doc[2].text == u'vous' assert doc[2].text == u'vous'
assert doc[3].text == u'français' assert doc[3].text == u'français'
assert doc[4].text == u'?' assert doc[4].text == u'?'
def test_load_dutch():
nlp = Dutch()
doc = nlp(u'Is dit Nederlands?')
assert doc[0].text == u'Is'
assert doc[1].text == u'dit'
assert doc[2].text == u'Nederlands'
assert doc[3].text == u'?'

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@ -47,7 +47,7 @@ p
+cell.u-text-center #[+procon(icon)] +cell.u-text-center #[+procon(icon)]
+row +row
+cell Entity Regonition +cell Entity Recognition
each icon in [ "pro", "con", "pro", "pro" ] each icon in [ "pro", "con", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)] +cell.u-text-center #[+procon(icon)]

View File

@ -217,7 +217,7 @@ p
('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')]) ('I like London and Berlin.', [(7, 13, 'LOC'), (18, 24, 'LOC')])
] ]
nlp = spacy.load(entity=False, parser=False) nlp = spacy.load('en', entity=False, parser=False)
ner = EntityRecognizer(nlp.vocab, entity_types=['PERSON', 'LOC']) ner = EntityRecognizer(nlp.vocab, entity_types=['PERSON', 'LOC'])
for itn in range(5): for itn in range(5):