Resolve stopwords conflict to merge Dutch

.github/contributors/RvanNieuwpoort.md

@@ -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
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
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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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+
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+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
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+    against us, our licensees or transferees;
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+    exercise all ownership rights associated with it; and
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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/  |

CONTRIBUTORS.md

@@ -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)
 * Chris DuBois, [@chrisdubois](https://github.com/chrisdubois)
 * Christoph Schwienheer, [@chssch](https://github.com/chssch)
+* Dafne van Kuppevelt, [@dafnevk](https://github.com/dafnevk)
 * Dmytro Sadovnychyi, [@sadovnychyi](https://github.com/sadovnychyi)
 * Henning Peters, [@henningpeters](https://github.com/henningpeters)
 * Ines Montani, [@ines](https://github.com/ines)
 * J Nicolas Schrading, [@NSchrading](https://github.com/NSchrading)
+* Janneke van der Zwaan, [@jvdzwaan](https://github.com/jvdzwaan)
 * Jordan Suchow, [@suchow](https://github.com/suchow)
 * Kendrick Tan, [@kendricktan](https://github.com/kendricktan)
 * 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)
 * Oleg Zd, [@olegzd](https://github.com/olegzd)
 * Pokey Rule, [@pokey](https://github.com/pokey)
+* Rob van Nieuwpoort, [@RvanNieuwpoort](https://github.com/RvanNieuwpoort)
 * Sam Bozek, [@sambozek](https://github.com/sambozek)
 * Sasho Savkov [@savkov](https://github.com/savkov)
 * Tiago Rodrigues, [@TiagoMRodrigues](https://github.com/TiagoMRodrigues)
 * Vsevolod Solovyov, [@vsolovyov](https://github.com/vsolovyov)
 * Wah Loon Keng, [@kengz](https://github.com/kengz)
+* Willem van Hage, [@wrvhage](https://github.com/wrvhage)
 * Wolfgang Seeker, [@wbwseeker](https://github.com/wbwseeker)
 * Yanhao Yang, [@YanhaoYang](https://github.com/YanhaoYang)
 * Yubing Dong, [@tomtung](https://github.com/tomtung)

bin/init_model.py

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

examples/training/load_ner.py

@@ -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)

examples/training/train_ner.py

@@ -10,6 +10,13 @@ from spacy.tagger import Tagger
 
 
 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)
     for itn in range(5):
         random.shuffle(train_data)
@@ -20,21 +27,30 @@ def train_ner(nlp, train_data, entity_types):
     ner.model.end_training()
     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):
-    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)
 
     # v1.1.2 onwards
     if nlp.tagger is None:
         print('---- WARNING ----')
         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('-----------------')
         nlp.tagger = Tagger(nlp.vocab, features=Tagger.feature_templates)
@@ -56,16 +72,17 @@ def main(model_dir=None):
     nlp.tagger(doc)
     ner(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:
-        with (model_dir / 'config.json').open('w') as file_:
-            json.dump(ner.cfg, file_)
-        ner.model.dump(str(model_dir / 'model'))
+        save_model(ner, model_dir)
+
+
+
 
 
 if __name__ == '__main__':
-    main()
+    main('ner')
     # Who "" 2
     # is "" 2
     # Shaka "" PERSON 3

examples/training/train_tagger.py

@@ -69,7 +69,7 @@ def main(output_dir=None):
         print(word.text, word.tag_, word.pos_)
     if output_dir is not None:
         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_)
 
 

setup.py

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

spacy/__init__.py

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

spacy/nl/__init__.py

@@ -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)

spacy/nl/language_data.py

@@ -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])m²
+(?<=[0-9])cm²
+(?<=[0-9])mm²
+(?<=[0-9])km³
+(?<=[0-9])m³
+(?<=[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"
+  }
+}

spacy/symbols.pxd

@@ -426,3 +426,9 @@ cpdef enum symbol_t:
 #IS_QUOTE
 #IS_LEFT_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
+

spacy/tests/integration/test_load_languages.py

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

website/docs/api/index.jade

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

website/docs/usage/entity-recognition.jade

@@ -217,7 +217,7 @@ p
         ('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'])
 
     for itn in range(5):