Datasets:
File size: 10,896 Bytes
cdeba15 7abaecc cdeba15 |
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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""NELL: Never Ending Language Learner"""
from html import unescape
from urllib.parse import unquote
import datasets
_CITATION = """\
@inproceedings{mitchell2015,
added-at = {2015-01-27T15:35:24.000+0100},
author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.},
biburl = {https://www.bibsonomy.org/bibtex/263070703e6bb812852cca56574aed093/hotho},
booktitle = {AAAI},
description = {Papers by William W. Cohen},
interhash = {52d0d71f6f5b332dabc1412f18e3a93d},
intrahash = {63070703e6bb812852cca56574aed093},
keywords = {learning nell ontology semantic toread},
note = {: Never-Ending Learning in AAAI-2015},
timestamp = {2015-01-27T15:35:24.000+0100},
title = {Never-Ending Learning},
url = {http://www.cs.cmu.edu/~wcohen/pubs.html},
year = 2015
}
"""
_DESCRIPTION = """This dataset provides version 1115 of the belief
extracted by CMU's Never Ending Language Learner (NELL) and version
1110 of the candidate belief extracted by NELL. See
http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information
extraction system that attempts to read the Clueweb09 of 500 million
web pages (http://boston.lti.cs.cmu.edu/Data/clueweb09/) and general
web searches.
The dataset has 4 configurations: nell_belief, nell_candidate,
nell_belief_sentences, and nell_candidate_sentences. nell_belief is
certainties of belief are lower. The two sentences config extracts the
CPL sentence patterns filled with the applicable 'best' literal string
for the entities filled into the sentence patterns. And also provides
sentences found using web searches containing the entities and
relationships.
There are roughly 21M entries for nell_belief_sentences, and 100M
sentences for nell_candidate_sentences.
"""
_LICENSE = """
"""
_URLs = {
"nell_belief": "http://rtw.ml.cmu.edu/resources/results/08m/NELL.08m.1115.esv.csv.gz",
"nell_candidate": "http://rtw.ml.cmu.edu/resources/results/08m/NELL.08m.1110.cesv.csv.gz",
"nell_belief_sentences": "http://rtw.ml.cmu.edu/resources/results/08m/NELL.08m.1115.esv.csv.gz",
"nell_candidate_sentences": "http://rtw.ml.cmu.edu/resources/results/08m/NELL.08m.1110.cesv.csv.gz",
}
class Nell(datasets.GeneratorBasedBuilder):
"""NELL dataset for knowledge bases and knowledge graphs and underlying sentences."""
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="nell_belief", description="The beliefs in raw data form", version="1115.0.0"),
datasets.BuilderConfig(
name="nell_candidate", description="The candidate beliefs in raw data form", version="1110.0.0"
),
datasets.BuilderConfig(
name="nell_belief_sentences",
description="The underlying sentences available for the nell beliefs",
version="1115.0.0",
),
datasets.BuilderConfig(
name="nell_candidate_sentences",
description="The underlying sentences available for the nell candidate beliefs",
version="1110.0.0",
),
]
DEFAULT_CONFIG_NAME = "nell"
def _info(self):
if self.config.name in ("nell_belief", "nell_candidate"):
features = datasets.Features(
{
"entity": datasets.Value("string"),
"relation": datasets.Value("string"),
"value": datasets.Value("string"),
"iteration_of_promotion": datasets.Value("string"),
"score": datasets.Value("string"),
"source": datasets.Value("string"),
"entity_literal_strings": datasets.Value("string"),
"value_literal_strings": datasets.Value("string"),
"best_entity_literal_string": datasets.Value("string"),
"best_value_literal_string": datasets.Value("string"),
"categories_for_entity": datasets.Value("string"),
"categories_for_value": datasets.Value("string"),
"candidate_source": datasets.Value("string"),
}
)
else:
features = datasets.Features(
{
"entity": datasets.Value("string"),
"relation": datasets.Value("string"),
"value": datasets.Value("string"),
"score": datasets.Value("string"),
"sentence": datasets.Value("string"),
"count": datasets.Value("int32"),
"url": datasets.Value("string"),
"sentence_type": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage="http://rtw.ml.cmu.edu/rtw/",
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples from the NELL belief knowledge base and candidate bleifs knowledge base if the config is 'nell_belief' and 'nell_candidate', respectively, otherwise yields the sentences for two dataset if the config is 'nell_belief_sentences' and 'nell_candidate_sentences' respectively."""
with open(filepath, encoding="utf-8") as f:
id_ = -1
for row in f:
row = row.strip().split("\t")
if "[" in row[3]:
row[3] = row[3].strip("[]").split(",")[0]
if "[" in row[4]:
row[4] = row[4].strip("[]").split(",")[0]
if self.config.name in ("nell_belief", "nell_candidate"):
id_ += 1
yield id_, {
"entity": row[0].strip(),
"relation": row[1].strip(),
"value": row[2].strip(),
"iteration_of_promotion": row[3].strip(),
"score": row[4].strip(),
"source": row[5].strip(),
"entity_literal_strings": row[6].strip(),
"value_literal_strings": row[7].strip(),
"best_entity_literal_string": row[8].strip(),
"best_value_literal_string": row[9].strip(),
"categories_for_entity": row[10].strip(),
"categories_for_value": row[11].strip(),
"candidate_source": row[12].strip(),
}
else:
best_arg1 = row[8]
best_arg2 = row[9]
iter_type = ""
for s2 in unquote(row[12]).strip("[]").split("-Iter"):
if iter_type in ("CPL", "OE"):
arr = unescape(s2.split(">", 1)[-1].strip("-").replace("+", " ")).split("\t")
la = len(arr)
count = 0
url = ""
for i in range(0, la, 2):
sentence = arr[i]
if i + 1 == la:
count = 1
url = ""
else:
try:
count = int(arr[i + 1].split(",")[0])
url = ""
except ValueError:
count = 1
url = ""
if arr[i + 1].startswith("http"):
url = arr[i + 1].split(",")[0]
if iter_type == "CPL":
if "_" in sentence:
sentence = sentence.replace("_", "[[ " + best_arg1 + " ]]")
elif "arg1" in sentence:
sentence = sentence.replace("arg1", "[[ " + best_arg1 + " ]]").replace(
"arg2", "[[ " + best_arg2 + " ]]"
)
else:
continue
if sentence.endswith("CPL"):
sentence = sentence[:-5]
if sentence.endswith("OE"):
sentence = sentence[:-4]
id_ += 1
yield id_, {
"entity": row[0].replace("candidate:", "").replace("concept:", "").strip(),
"relation": row[1].replace("candidate:", "").replace("concept:", "").strip(),
"value": row[2].replace("candidate:", "").replace("concept:", "").strip(),
"score": row[4].strip(),
"sentence": sentence.strip(),
"count": int(count),
"url": url.strip(),
"sentence_type": iter_type,
}
iter_type = s2.split(",")[-1].strip("+")
|