def test_case_5(self):
data = get_tagged_texts_as_pd(self.folders,
'../../../data/datasets/gmb-2.2.0')
data = filtrations(data, with_dots=True)
bio_ner_tags = iob3bio(data.ner_tag.values.tolist())
self.assertTrue(len(bio_ner_tags) == len(data))
def test_case_6(self):
ner_tags = iob3bio([
'per-tit', 'per-nam', 'per-nam', 'per-nam', 'O', 'O', 'O', 'O',
'gpe-tit', 'gpe-nam', 'gpe-nam', 'gpe-tit', 'gpe-tit', 'O', 'O',
'O', 'O', 'O', 'O'
])
self.assertEqual(ner_tags, [
'B-per', 'B-per', 'I-per', 'I-per', 'O', 'O', 'O', 'O', 'B-gpe',
'B-gpe', 'I-gpe', 'B-gpe', 'I-gpe', 'O', 'O', 'O', 'O', 'O', 'O'
], 'Something is wrong!!!')
def setUpClass(cls):
folders = filter_by_subcorpus('../../../data/datasets/gmb-2.2.0',
'subcorpus: Voice of America')
data = get_tagged_texts_as_pd(folders,
'../../../data/datasets/gmb-2.2.0')
data = filtrations(data, with_dots=True)
data.ner_tag = iob3bio(data.ner_tag.values)
data = additional_features(df=data)
cls.features = data.columns.values.tolist()
cls.features.remove('ner_tag')
cls.features.remove('word_net_sense_number')
cls.features.remove('verb_net_roles')
cls.features.remove('semantic_relation')
cls.features.remove('animacy_tag')
cls.features.remove('super_tag')
cls.features.remove('lambda_dsr')
X, y = SentenceExtractor(features=cls.features,
target='ner_tag').fit_transform(data)
cls.X_train, cls.X_test, cls.y_train, cls.y_test = train_test_split(
X, y, test_size=0.33, random_state=42)
cls.X_train = [
sentence for sentence in cls.X_train if len(sentence) > 0
]
cls.y_train = [
sentence.tolist() for sentence in cls.y_train if len(sentence) > 0
]
cls.X_test = [sentence for sentence in cls.X_test if len(sentence) > 0]
cls.y_test = [
sentence.tolist() for sentence in cls.y_test if len(sentence) > 0
]
def setUpClass(cls):
folders = filter_by_subcorpus('../../../data/datasets/gmb-2.2.0', 'subcorpus: Voice of America')
data = get_tagged_texts_as_pd(folders, '../../../data/datasets/gmb-2.2.0')
data = filtrations(data, with_dots=True)
data.ner_tag = iob3bio(data.ner_tag.values)
cls.X, cls.y = SentenceExtractor(
features=[
'token',
'pos_tag',
'lemma'
],
target='ner_tag'
).fit_transform(data)
def setUpClass(cls):
folders = filter_by_subcorpus('../../../data/datasets/gmb-2.2.0', 'subcorpus: Voice of America')
data = get_tagged_texts_as_pd(folders, '../../../data/datasets/gmb-2.2.0')
data = filtrations(data, with_dots=True)
data.ner_tag = iob3bio(data.ner_tag.values)
data = additional_features(df=data)
# features list:
cls.features = [
'token',
'lemma',
'pos_tag',
'is_title',
'contains_digits',
'word_len',
'suffix',
'prefix',
'prev_pos_tag',
'prev_is_title',
'prev_contains_digits',
'prev_word_len',
'prev_suffix',
'prev_prefix',
'next_pos_tag',
'next_is_title',
'next_contains_digits',
'next_word_len',
'next_suffix',
'next_prefix'
]
X, y = SentenceExtractor(features=cls.features, target='ner_tag').fit_transform(data)
cls.X_train, cls.X_test, cls.y_train, cls.y_test = train_test_split(X, y, test_size=0.33, random_state=42)
cls.X_train = [sentence for sentence in cls.X_train if len(sentence) > 0]
cls.y_train = [sentence.tolist() for sentence in cls.y_train if len(sentence) > 0]
cls.X_test = [sentence for sentence in cls.X_test if len(sentence) > 0]
cls.y_test = [sentence.tolist() for sentence in cls.y_test if len(sentence) > 0]
def setUpClass(cls):
folders = filter_by_subcorpus('../../../data/datasets/gmb-2.2.0',
'subcorpus: Voice of America')
data = get_tagged_texts_as_pd(folders,
'../../../data/datasets/gmb-2.2.0')
data = filtrations(data, with_dots=True)
data.ner_tag = iob3bio(data.ner_tag.values)
cls.features = ['token', 'pos_tag', 'lemma']
X, y = SentenceExtractor(features=cls.features,
target='ner_tag').fit_transform(data)
cls.X_train, cls.X_test, cls.y_train, cls.y_test = train_test_split(
X, y, test_size=0.33, random_state=42)