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