def test_fallback_labeling(self):
file = "res/TIGER/tiger21/tigertraindev_root_attach.export"
corpus = np.sentence_names_to_hybridtrees(
[str(x) for x in range(50) if x % 10 > 1],
file,
disconnect_punctuation=False)
labeling = tl.FrequencyBiasedTerminalLabeling(tl.FormTerminals(),
tl.PosTerminals(),
corpus=corpus,
threshold=2)
print(labeling.fine_label_count)
token1 = mt.ConstituentTerminal('Milliardär', 'NN')
token2 = mt.ConstituentTerminal('Tisch', 'NN')
label1 = labeling.token_label(token1)
label2 = labeling.token_label(token2)
f = io.StringIO()
json.dump(labeling.serialize(), f)
f.seek(0)
print(f.getvalue())
instance2 = tl.deserialize_labeling(json.load(f))
self.assertTrue(isinstance(instance2, labeling.__class__))
self.assertEqual(label1, instance2.token_label(token1))
self.assertEqual(label2, instance2.token_label(token2))
def test_composition_labeling(self):
complex = tl.CompositionalTerminalLabeling(tl.FormTerminals(), tl.PosTerminals(), binding_string='/')
token = mt.ConstituentTerminal('Tisch', 'NN')
label = complex.token_label(token)
print(token, label)
serialization = complex.serialize()
print(serialization)
instance2 = tl.deserialize_labeling(serialization)
self.assertTrue(isinstance(instance2, complex.__class__))
self.assertEqual(label, instance2.token_label(token))