Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
machine-generated
Annotations Creators:
found
Source Datasets:
original
License:
File size: 5,839 Bytes
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# 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.
"""TupleInf Open IE Dataset"""
import os
import datasets
_CITATION = """\
@article{Khot2017AnsweringCQ,
title={Answering Complex Questions Using Open Information Extraction},
author={Tushar Khot and A. Sabharwal and Peter Clark},
journal={ArXiv},
year={2017},
volume={abs/1704.05572}
}
"""
_DESCRIPTION = """\
The TupleInf Open IE dataset contains Open IE tuples extracted from 263K sentences that were used by the solver \
in “Answering Complex Questions Using Open Information Extraction” (referred as Tuple KB, T). \
These sentences were collected from a large Web corpus using training questions from 4th and 8th grade as queries. \
This dataset contains 156K sentences collected for 4th grade questions and 107K sentences for 8th grade questions. \
Each sentence is followed by the Open IE v4 tuples using their simple format.
"""
_HOMEPAGE = "https://allenai.org/data/tuple-ie"
_URL = "https://ai2-public-datasets.s3.amazonaws.com/tuple-ie/TupleInfKB.zip"
_DOMAIN_FILES = {"4th_grade": "4thGradeOpenIE.txt", "8th_grade": "8thGradeOpenIE.txt"}
class TupleIEConfig(datasets.BuilderConfig):
"""BuilderConfig for TupleIE"""
def __init__(self, *args, domains=None, **kwargs):
super().__init__(*args, **kwargs)
self.domains = domains
class TupleIE(datasets.GeneratorBasedBuilder):
"""TupleInf Open IE Dataset"""
BUILDER_CONFIGS = [
TupleIEConfig(
name="all",
domains=list(_DOMAIN_FILES.keys()),
description="collected using training questions from 4th and 8th grade as queries.",
)
] + [
TupleIEConfig(
name=name, domains=[name], description=f"collected using training questions from {name} as queries."
)
for name in _DOMAIN_FILES.keys()
]
BUILDER_CONFIG_CLASS = TupleIEConfig
DEFAULT_CONFIG_NAME = "all"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sentence": datasets.Value("string"),
"tuples": datasets.features.Sequence(
{
"score": datasets.Value("float"),
"tuple_text": datasets.Value("string"),
"context": datasets.Value("string"),
"arg1": datasets.Value("string"),
"rel": datasets.Value("string"),
"arg2s": datasets.features.Sequence(datasets.Value("string")),
}
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = os.path.join(dl_manager.download_and_extract(_URL), "TupleInfKB")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": data_dir},
)
]
def _generate_examples(self, data_dir):
"""Yields examples."""
id_ = -1
for domain in self.config.domains:
with open(os.path.join(data_dir, _DOMAIN_FILES[domain]), encoding="utf-8") as f:
all_text = f.read()
samples = all_text.split("\n\n")
for sample in samples:
rows = sample.split("\n")
item = {"sentence": rows[0], "tuples": []}
tuple_lines = rows[1:]
for tuple_line in tuple_lines:
score, tuple_text = tuple_line.split(" ", 1)
context, arg1, rel, arg2s = self._decode_tuple_text(tuple_text)
item["tuples"].append(
{
"score": score,
"tuple_text": tuple_text,
"context": context,
"arg1": arg1,
"rel": rel,
"arg2s": arg2s,
}
)
id_ += 1
yield id_, item
def _decode_tuple_text(self, tuple_text):
"""Decompose the tuple text into arguments and relations
Args:
tuple_text (str): Format of extraction text:
```
{Context(<context>):}(<arg1>; <rel>; {[L|T]:}<arg2_1>; {[L|T]:}<arg2_2>; ...)
```
.. note::
* ``{}`` means one can be optionally appear
* ``[L|T]`` means ``L`` or ``T``
* ``L`` means spatial/location argument
* ``T`` means temporal argument
* We can have multiple arg2s
"""
context = ""
arg1 = ""
rel = ""
arg2s = []
if tuple_text.startswith("Context("):
context, tuple_text = tuple_text.split(":", 1)
context = context[len("Context(") : -1]
args = tuple_text[1:-1].split("; ")
arg1, rel = args[:2]
arg2s = args[2:]
return context, arg1, rel, arg2s
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