def test_example1(self):
tokens = []
tokens.append(
Token(1, b'Kiten', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Master', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(3, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(4,
b'http://youtu.be/jVVD0OZk-6g',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'Kiten Master: http://youtu.be/jVVD0OZk-6g'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 4, 1, 1, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_extract_average_polarity_example1(self):
tokens = []
tokens.append(Token(1, None, None, None, None, None, None, 0.5))
tokens.append(Token(2, None, None, None, None, None, None, None))
tokens.append(Token(3, None, None, None, None, None, None, 1.5))
thf_sentence = THFSentenceExport(None, None, None, tokens, None, 1)
feature_value = polarity_sentiws_feature.extract_average_polarity(
thf_sentence)
expected_value = [1.0]
self.assertEqual(feature_value, expected_value)
def test_count_polarity_bearing_tokens_example1(self):
tokens = []
tokens.append(Token(1, None, None, None, None, None, None, 0.5))
tokens.append(Token(2, None, None, None, None, None, None, None))
tokens.append(Token(3, None, None, None, None, None, None, 1.5))
thf_sentence = THFSentenceExport(None, None, None, tokens, None, 1)
feature_value = sentiws_polarity_bearing_tokens_feature.count_polarity_bearing_tokens(
thf_sentence)
expected_value = [2]
self.assertEqual(feature_value, expected_value)
def test_example6(self):
tokens = []
tokens.append(
Token(1, b'kleine', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2,
b'Elektrodrohnen',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(3, b'just', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(4, b'for', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(5, b'fun', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b',', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7, b'warum', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(8, b'nicht', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(9, b'.', spacy_is_punct=True,
spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'kleine Elektrodrohnen just for fun, warum nicht.'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 1.0 / len(tokens), 1.0 / len(tokens), 9, 0, 2, 1.0, 0.0, 0.0,
0.0
]
self.assertEqual(feature_value, expected_value)
def test_exclamation_mark_end(self):
tokens = []
tokens.append(Token(1, 'Test', None, None, None, None, None, None))
tokens.append(Token(2, '!', None, None, None, None, None, None))
sentence_uniqueID = 'p339_s007'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, 'Test !',
tokens, None, 1)
use_sentence_length = False
feature_value = structural_features.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
7, 0.0 / len(tokens), 0.0 / len(tokens), 0.0, 0.0, 1.0, 0.0, 0.0
]
self.assertEqual(feature_value, expected_value)
def test_example4(self):
tokens = []
tokens.append(
Token(1, b'Hier', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'eine', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(3, b'Konzept', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(4, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(5, b'Grafik', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7, b' ', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(8,
b'http://i.imgur.com/JGlqExO.jpg',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'Hier eine Konzept-Grafik: http://i.imgur.com/JGlqExO.jpg'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 8, 1, 2, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def load_v3(file_path='data/THF/sentence/subtaskA_train.json',
group_claims=True):
logger.debug(u'Parsing JSON File: {}'.format(file_path))
sentences = []
with open(file_path, encoding='utf-8') as data_file:
data = json.load(data_file)
for sentence in data:
sentence_tokens = sentence["NLP"]["tokens"]
tokens = []
for token in sentence_tokens:
token.pop("embedding")
token_model = Token(**token)
tokens.append(token_model)
dependencies = []
dependency_tokens = sentence["NLP"]["dependencies"]
for dependency in dependency_tokens:
dependency_model = Dependency(**dependency)
dependencies.append(dependency_model)
label = sentence["Label"]
if group_claims:
if label == 'ClaimContra' or label == 'ClaimPro':
label = 'Claim'
sentence_model = THFSentenceExport(sentence["UniqueID"],
label,
sentence["Text"],
tokens,
dependencies,
textdepth=sentence["TextDepth"])
sentences.append(sentence_model)
logger.info('Parsed {} sentences'.format(len(sentences)))
return sentences
def process_sentence(self, sentence):
result = self.nlp(sentence)
tokens = []
dependencies = []
for token in result:
iwnlp_lemma = self.lemmatizer.lemmatize(
token.text, pos_universal_google=token.pos_)
sentiws = self.sentiws.determine(token.text,
pos_universal_google=token.pos_)
token_model = Token(token.i + 1,
text=token.text,
spacy_pos_stts=token.tag_,
spacy_pos_universal_google=token.pos_,
iwnlp_lemma=iwnlp_lemma,
spacy_ner_type=token.ent_type_,
spacy_ner_iob=token.ent_iob_,
spacy_is_punct=token.is_punct,
spacy_is_space=token.is_space,
spacy_like_num=token.like_num,
spacy_like_url=token.like_url,
spacy_shape=token.shape_,
polarity_sentiws=sentiws)
tokens.append(token_model)
dependency_model = Dependency(token.i + 1, token.dep_,
token.head.i + 1)
dependencies.append(dependency_model)
# print(token_model.token_index_in_sentence, token_model.text.encode('utf-8'),
# format_iwnlp_lemma(token_model.iwnlp_lemma), token_model.spacy_pos_stts,
# token_model.spacy_pos_universal_google, token_model.spacy_ner_type, token_model.spacy_ner_iob)
return {'tokens': tokens, 'dependencies': dependencies}
def test_link(self):
tokens = []
tokens.append(Token(1, 'Test', None, None, None, None, None, None))
tokens.append(
Token(2, 'http://umap.openstreetmap.fr', None, None, None, None,
None, None))
sentence_uniqueID = 'p339_s021'
thf_sentence = THFSentenceExport(sentence_uniqueID, None,
'Test http://umap.openstreetmap.fr',
tokens, None, 1)
use_sentence_length = False
feature_value = structural_features.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
21, 0.0 / len(tokens), 0.0 / len(tokens), 1.0, 0.0, 0.0, .0, 1.0
]
self.assertEqual(feature_value, expected_value)
def test_count_different_ner_labels_example5(self):
tokens = []
tokens.append(
Token(1,
b'Vorbild',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(2,
b'New',
spacy_pos_universal_google='PROPN',
spacy_ner_type='LOC',
spacy_ner_iob='B'))
tokens.append(
Token(3,
b'York',
spacy_pos_universal_google='PROPN',
spacy_ner_type='LOC',
spacy_ner_iob='I'))
tokens.append(
Token(4,
b':',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(
5,
b'http://www.houndsandpeople.com/de/magazin/kultur/new-york-city-dogs-teil-und-seele-der-weltmetropole/',
spacy_pos_universal_google='AUX',
spacy_ner_type='',
spacy_ner_iob='O'))
thf_sentence = THFSentenceExport(
None, None,
"Vorbild New York: http://www.houndsandpeople.com/de/magazin/kultur/new-york-city-dogs-teil-und-seele-der-weltmetropole/",
tokens, None, 1)
feature_value = ner_feature.count_different_ner_labels(
thf_sentence.tokens)
expected_value = np.array([0, 1, 0], dtype=np.float64)
self.assertEqual(np.array_equal(feature_value, expected_value), True)
def load(file_path='data/THF/sentence/subtaskA_train.json', group_claims=True):
"""
Loads the THF corpus from an JSON file
:param file_path: relative path to the JSON file
:return:
"""
logger.debug(u'Parsing JSON File: {}'.format(file_path))
sentences = []
with open(file_path, encoding='utf-8') as data_file:
data = json.load(data_file)
for sentence in data:
sentence_tokens = sentence["NLP"]["Sentences"][0]["Tokens"]
tokens = []
dependencies = []
for token in sentence_tokens:
token_model = Token(token["TokenIndexInSentence"],
token["Text"],
pos_tag=token["POSTag"],
mate_tools_pos_tag=token["MateToolsPPOS"],
mate_tools_lemma=token["MateToolsPLemma"],
tree_tagger_lemma=parse_tree_tagger_lemma(
token.get("TreeTaggerLemma", None)),
iwnlp_lemma=parse_IWNLP_lemma(
token.get("IWNLPLemma", None)),
polarity=parse_polarity(
(token.get("Polarity", None))))
tokens.append(token_model)
dependency_tokens = sentence["NLP"]["Sentences"][0]["Dependencies"]
for dependency in dependency_tokens:
dependency_model = Dependency(
dependency["TokenID"], dependency["DependencyRelation"],
dependency["DependencyHeadTokenID"])
dependencies.append(dependency_model)
label = sentence["Label"]
if group_claims:
if label == 'ClaimContra' or label == 'ClaimPro':
label = 'Claim'
sentence_model = THFSentenceExport(sentence["UniqueID"], label,
sentence["Text"], tokens,
dependencies)
sentences.append(sentence_model)
logger.info('Parsed {} sentences'.format(len(sentences)))
return sentences
def test_example2_without_sentence_length(self):
tokens = []
tokens.append(Token(1, 'Das', None, None, None, None, None, None))
tokens.append(Token(2, 'ist', None, None, None, None, None, None))
tokens.append(Token(3, '.', None, None, None, None, None, None))
tokens.append(Token(4, ',', None, None, None, None, None, None))
tokens.append(Token(5, 'Test', None, None, None, None, None, None))
tokens.append(Token(6, '!', None, None, None, None, None, None))
sentence_uniqueID = 'p339_s021'
thf_sentence = THFSentenceExport(sentence_uniqueID, None,
'Das ist . , Test!', tokens, None, 1)
use_sentence_length = False
feature_value = structural_features.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
21, 1.0 / len(tokens), 1.0 / len(tokens), 0.0, 0.0, 1.0, 0.0, 0.0
]
self.assertEqual(feature_value, expected_value)
def test_example1(self):
tokens = []
tokens.append(Token(1, 'Das', None, None, None, None, None, None))
tokens.append(Token(2, 'ist', None, None, None, None, None, None))
tokens.append(Token(3, 'ein', None, None, None, None, None, None))
tokens.append(Token(4, ',', None, None, None, None, None, None))
tokens.append(Token(5, 'Test', None, None, None, None, None, None))
tokens.append(Token(6, '!', None, None, None, None, None, None))
sentence_uniqueID = 'c0331_s003'
thf_sentence = THFSentenceExport(sentence_uniqueID, None,
'Das ist ein , Test!', tokens, None,
1)
use_sentence_length = True
feature_value = structural_features.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 1.0 / len(tokens), 0.0 / len(tokens), 1.0 * len(tokens), 0.0,
0.0, 1.0, 0.0, 0.0
]
self.assertEqual(feature_value, expected_value)
def test_example5(self):
tokens = []
tokens.append(
Token(1,
b'Tempelhofparikram',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(Token(2, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(3,
b'Interreligi\xc3\xb6ser',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(4, b'Pilgerpfad', spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(5, b'auf', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(6, b'dem', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(7,
b'Tempelhofer',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(8, b'Feld', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(9, b'(', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(10,
b'http://lebensplan.com/Interreligioeser-Pilgerpfad.pdf',
spacy_is_punct=False,
spacy_like_url=True))
tokens.append(
Token(11, b')', spacy_is_punct=True, spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'Tempelhofparikram - Interreligi\u00f6ser Pilgerpfad auf dem Tempelhofer Feld (http://lebensplan.com/Interreligioeser-Pilgerpfad.pdf)'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 11, 1, 3, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_count_different_ner_labels_example1(self):
tokens = []
tokens.append(
Token(1,
b'Wenn',
spacy_pos_universal_google='SCONJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(2,
b'ich',
spacy_pos_universal_google='PRON',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(3,
b'durch',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(4,
b'den',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(5,
b'Hans',
spacy_pos_universal_google='PROPN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(6,
b'-',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(7,
b'Baluschek',
spacy_pos_universal_google='PROPN',
spacy_ner_type='PERSON',
spacy_ner_iob='B'))
tokens.append(
Token(8,
b'-',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(9,
b'Park',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(10,
b'radle',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(11,
b',',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(12,
b'riecht',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(13,
b'es',
spacy_pos_universal_google='PRON',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(14,
b'immer',
spacy_pos_universal_google='ADV',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(15,
b'stark',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(16,
b'vom',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(17,
b'angrenzenden',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(18,
b'S\xc3\xbcdgel\xc3\xa4nde',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(19,
b'nach',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(20,
b'Farbd\xc3\xbcnsten',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(21,
b'und',
spacy_pos_universal_google='CONJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(22,
b'das',
spacy_pos_universal_google='PRON',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(23,
b'passt',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(24,
b'nicht',
spacy_pos_universal_google='PART',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(25,
b'wirklich',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(26,
b'zum',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(27,
b'Naturschutz',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(28,
b'.',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
thf_sentence = THFSentenceExport(
None, None,
"Wenn ich durch den Hans-Baluschek-Park radle, riecht es immer stark vom angrenzenden Südgelände nach Farbdünsten und das passt nicht wirklich zum Naturschutz.",
tokens, None, 1)
feature_value = ner_feature.count_different_ner_labels(
thf_sentence.tokens)
expected_value = np.array([1, 0, 0], dtype=np.float64)
self.assertEqual(np.array_equal(feature_value, expected_value), True)
def test_count_different_ner_labels_example4(self):
tokens = []
tokens.append(
Token(1,
b'Oder',
spacy_pos_universal_google='CONJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(2,
b',',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(3,
b'um',
spacy_pos_universal_google='SCONJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(4,
b'mit',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(5,
b'Hermann',
spacy_pos_universal_google='PROPN',
spacy_ner_type='PERSON',
spacy_ner_iob='B'))
tokens.append(
Token(6,
b'Hesse',
spacy_pos_universal_google='PROPN',
spacy_ner_type='PERSON',
spacy_ner_iob='I'))
tokens.append(
Token(7,
b'zu',
spacy_pos_universal_google='PART',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(8,
b'sprechen',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(9,
b':',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(10,
b'Jedem',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(11,
b'Ende',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(12,
b'wohnt',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(13,
b'ein',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(14,
b'neuer',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(15,
b'Anfang',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(16,
b'inne',
spacy_pos_universal_google='PART',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(17,
b'.',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
thf_sentence = THFSentenceExport(
None, None,
"Oder, um mit Hermann Hesse zu sprechen: Jedem Ende wohnt ein neuer Anfang inne.",
tokens, None, 1)
feature_value = ner_feature.count_different_ner_labels(
thf_sentence.tokens)
expected_value = np.array([1, 0, 0], dtype=np.float64)
self.assertEqual(np.array_equal(feature_value, expected_value), True)
def test_count_different_ner_labels_example3(self):
tokens = []
tokens.append(
Token(1,
b'Auf',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(2,
b'dem',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(3,
b'Tempelhofer',
spacy_pos_universal_google='ADJ',
spacy_ner_type='LOC',
spacy_ner_iob='B'))
tokens.append(
Token(4,
b'Feld',
spacy_pos_universal_google='NOUN',
spacy_ner_type='LOC',
spacy_ner_iob='I'))
tokens.append(
Token(5,
b'stehen',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(6,
b'22',
spacy_pos_universal_google='NUM',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(7,
b'kleinere',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(8,
b'Geb\xc3\xa4ude',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(9,
b',',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(10,
b'gr\xc3\xb6\xc3\x9ftenteils',
spacy_pos_universal_google='ADV',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(11,
b'ungenutzt',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(12,
b'.',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
thf_sentence = THFSentenceExport(
None, None,
"Auf dem Tempelhofer Feld stehen 22 kleinere Gebäude, größtenteils ungenutzt.",
tokens, None, 1)
feature_value = ner_feature.count_different_ner_labels(
thf_sentence.tokens)
expected_value = np.array([0, 1, 0], dtype=np.float64)
self.assertEqual(np.array_equal(feature_value, expected_value), True)
def test_count_different_ner_labels_example2(self):
tokens = []
tokens.append(
Token(1,
b'Bei',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(2,
b'der',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(3,
b'Stauraumplanung',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(4,
b'wird',
spacy_pos_universal_google='AUX',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(5,
b'es',
spacy_pos_universal_google='PRON',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(6,
b'aus',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(7,
b'allen',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(8,
b'Gullis',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(9,
b'demn\xc3\xa4chst',
spacy_pos_universal_google='ADV',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(10,
b'stinken',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(11,
b'und',
spacy_pos_universal_google='CONJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(12,
b'Abwaaserlagerung',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(13,
b'in',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(14,
b'Schiffen',
spacy_pos_universal_google='NOUN',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(15,
b'auf',
spacy_pos_universal_google='ADP',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(16,
b'der',
spacy_pos_universal_google='DET',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(17,
b'Spree',
spacy_pos_universal_google='PROPN',
spacy_ner_type='LOC',
spacy_ner_iob='B'))
tokens.append(
Token(18,
b'd\xc3\xbcrfte',
spacy_pos_universal_google='VERB',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(19,
b'auch',
spacy_pos_universal_google='ADV',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(20,
b'nicht',
spacy_pos_universal_google='PART',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(21,
b'gesund',
spacy_pos_universal_google='ADJ',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(22,
b'sein',
spacy_pos_universal_google='AUX',
spacy_ner_type='',
spacy_ner_iob='O'))
tokens.append(
Token(23,
b'.',
spacy_pos_universal_google='PUNCT',
spacy_ner_type='',
spacy_ner_iob='O'))
thf_sentence = THFSentenceExport(
None, None,
"Bei der Stauraumplanung wird es aus allen Gullis demnächst stinken und Abwaaserlagerung in Schiffen auf der Spree dürfte auch nicht gesund sein.",
tokens, None, 1)
feature_value = ner_feature.count_different_ner_labels(
thf_sentence.tokens)
expected_value = np.array([0, 1, 0], dtype=np.float64)
self.assertEqual(np.array_equal(feature_value, expected_value), True)
def test_example2(self):
tokens = []
tokens.append(
Token(1, b'Diesen', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Vorschlag', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(3, b'gibt', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(4, b'es', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(5, b'schon', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(6, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(7,
b'S\xc3\xbcdlicher',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(8, b'Zugang', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(9, b'zur', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(10,
b'Oberlandstra\xc3\x9fe',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(11, b'(', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(12, b'Hatun', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(13, b'-', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(14,
b'S\xc3\xbcr\xc3\xbcc\xc3\xbc',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(15, b'-', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(16,
b'Br\xc3\xbccke',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(17, b')', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(18, b'(', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(
19,
b'https://tempelhofer-feld.berlin.de/i/tempelhofer-feld/proposal/104-S%C3%BCdlicher_Zugang_zur_Oberlandstra%C3%9Fe_Hatu',
spacy_is_punct=False,
spacy_like_url=True))
tokens.append(
Token(20, b')', spacy_is_punct=True, spacy_like_url=False))
sentence_uniqueID = 'p339_s003'
text = 'Diesen Vorschlag gibt es schon: S\u00fcdlicher Zugang zur Oberlandstra\u00dfe (Hatun-S\u00fcr\u00fcc\u00fc-Br\u00fccke) (https://tempelhofer-feld.berlin.de/i/tempelhofer-feld/proposal/104-S%C3%BCdlicher_Zugang_zur_Oberlandstra%C3%9Fe_Hatu)'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 20, 1, 7, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)
def test_example3(self):
tokens = []
tokens.append(
Token(1, b'BM', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(2, b'Tester', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(3, b'#', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(4, b'1', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(5, b':', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(6, b'Kite', spacy_is_punct=False, spacy_like_url=False))
tokens.append(Token(7, b'-', spacy_is_punct=True,
spacy_like_url=False))
tokens.append(
Token(8, b'Skaten', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(9, b'auf', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(10, b'dem', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(11,
b'Tempelhofer',
spacy_is_punct=False,
spacy_like_url=False))
tokens.append(
Token(12, b'Feld', spacy_is_punct=False, spacy_like_url=False))
tokens.append(
Token(13, b':', spacy_is_punct=True, spacy_like_url=False))
tokens.append(
Token(14,
b'http://youtu.be/Jf68D61QN4A',
spacy_is_punct=False,
spacy_like_url=True))
sentence_uniqueID = 'p339_s003'
text = 'BM Tester #1: Kite-Skaten auf dem Tempelhofer Feld: http://youtu.be/Jf68D61QN4A'
thf_sentence = THFSentenceExport(sentence_uniqueID, None, text, tokens,
None, 1)
use_sentence_length = True
feature_value = structural_features_spacy.transform_sentence(
thf_sentence, use_sentence_length)
expected_value = [
3, 0.0 / len(tokens), 0.0 / len(tokens), 14, 1, 4, 0.0, 0.0, 0.0,
1.0
]
self.assertEqual(feature_value, expected_value)