Datasets:
File size: 2,670 Bytes
2bb6f56 59a0bfa 2bb6f56 59a0bfa 2bb6f56 59a0bfa 2bb6f56 59a0bfa 2bb6f56 59a0bfa 2bb6f56 59a0bfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import datasets
import xml.etree.cElementTree as ET
from glob import glob
import os
_UFSAC_FILE = 'ufsac-public-2.1.tar.xz'
class UFSAC(datasets.GeneratorBasedBuilder):
BUILDER_CONFIG_CLASS = datasets.BuilderConfig
def _info(self):
feature = {
'tokens': datasets.Sequence(datasets.Value('string')),
'lemmas': datasets.Sequence(datasets.Value('string')),
'pos_tags': datasets.Sequence(datasets.Value('string')),
'target_idx': datasets.Value('int32'),
'sense_keys': datasets.Sequence(datasets.Value('string')),
}
return datasets.DatasetInfo(
features=datasets.Features(feature),
description = 'UFSAC: the unified Sense Annotated Corpora and Tool'
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_UFSAC_FILE)
return datasets.SplitGenerator(name = datasets.Split.TRAIN, gen_kwargs={'data_dir': data_dir}),
def _generate_examples(self, data_dir):
used_sents = set()
count = 0
for file in glob(os.path.join(data_dir, 'ufsac-public-2.1/*.xml')):
context = ET.iterparse(file, events=('start', 'end'))
event, root = next(context)
for event, element in context:
if element.tag == 'paragraph':
para = element
if element.tag != 'sentence':
continue
if event == 'end' and element.tag == 'sentence':
para.remove(element)
sent = element
words = sent.findall('word')
tokens = [token.attrib['surface_form'] if 'surface_form' in token.attrib else '_' for token in words]
sent_key = ''.join([token.lower() for token in tokens])
if sent_key in used_sents:
continue
used_sents.add(sent_key)
lemmas = [token.attrib['lemma'] if 'lemma' in token.attrib else '_' for token in words]
pos_tags = [token.attrib['pos'] if 'pos' in token.attrib else '_' for token in words]
for index, word in enumerate(words):
if 'wn30_key' in word.attrib:
senses = word.attrib['wn30_key'].split(';')
yield count, {
'tokens': tokens,
'lemmas': lemmas,
'pos_tags': pos_tags,
'target_idx': index,
'sense_keys': senses
}
count+=1
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