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"""Overview of FACT at IberLEF 2020: Events Detection and Classification""" |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{fact2020, |
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title = "Overview of FACT at IberLEF 2020: Events Detection and Classification", |
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author = "Rosa, Aiala and Chiruzzo, Luis and Wonsever, Dina and Malcuori, Marisa and Curell, Hortènsia and Castellón, Irene and Vázquez, Gloria and Fernández-Montraveta, Ana and Góngora, Santiago and Alonso, Laura", |
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booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", |
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year = "2020", |
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url = "https://www.aclweb.org/anthology/W03-0419", |
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} |
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""" |
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_DESCRIPTION = """\ |
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In this paper we present the second edition of the FACT shared task (Factuality Annotation and Classification |
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Task), included in IberLEF2020. The main objective of this task is to advance in the study of the factuality of |
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the events mentioned in texts. This year, the FACT task includes a subtask on event identification in addition |
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to the factuality classification subtask. We describe the submitted systems as well as the corpus used, which is |
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the same used in FACT 2019 but extended by adding annotations for nominal events. |
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""" |
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_URL = "https://huggingface.co/datasets/filevich/fact2020/raw/main/" |
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_URLS = { |
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"train": _URL + "train.json", |
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"validation": _URL + "validation.json", |
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"test": _URL + "test-task1.json", |
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} |
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class Fact2020Config(datasets.BuilderConfig): |
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"""BuilderConfig for Fact2020""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig forFact2020. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Fact2020Config, self).__init__(**kwargs) |
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class Fact2020(datasets.GeneratorBasedBuilder): |
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"""Fact2020 dataset.""" |
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BUILDER_CONFIGS = [ |
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Fact2020Config(name="fact2020", version=datasets.Version("1.0.0"), description="Fact2020 dataset"), |
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] |
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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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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"fact_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"F", |
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"CF", |
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"U", |
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] |
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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="https://ceur-ws.org/Vol-2664/fact_overview.pdf", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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urls_to_download = _URLS |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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import json |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f) |
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return [(i, {"id": str(i), **d}) for i,d in enumerate(data)] |
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