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import glob |
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import json |
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import os |
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from dataclasses import dataclass |
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import datasets |
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from datasets import BuilderConfig, SplitGenerator |
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_CITATION = """\ |
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@article{yang2018scidtb, |
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title={Scidtb: Discourse dependency treebank for scientific abstracts}, |
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author={Yang, An and Li, Sujian}, |
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journal={arXiv preprint arXiv:1806.03653}, |
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year={2018} |
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} |
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""" |
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_DESCRIPTION = """Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question |
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answering. SciDTB is a domain-specific discourse treebank annotated on scientific articles. |
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Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is |
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flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, |
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annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating |
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discourse dependency parsers, on which we provide several baselines as fundamental work.""" |
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_URL = "https://codeload.github.com/PKU-TANGENT/SciDTB/zip/refs/heads/master" |
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_HOMEPAGE = "https://github.com/PKU-TANGENT/SciDTB" |
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@dataclass |
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class SciDTBConfig(BuilderConfig): |
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"""BuilderConfig for SciDTB.""" |
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def __init__(self, subdirectory_mapping, encoding, **kwargs): |
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super(SciDTBConfig, self).__init__(**kwargs) |
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self.subdirectory_mapping = subdirectory_mapping |
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self.encoding = encoding |
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class SciDTBDataset(datasets.GeneratorBasedBuilder): |
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"""Scientific Discourse Treebank Dataset""" |
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BUILDER_CONFIGS = [ |
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SciDTBConfig( |
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name="SciDTB", |
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version=datasets.Version("1.0.0", ""), |
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description=_DESCRIPTION, |
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subdirectory_mapping={ |
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"train": "SciDTB-master/dataset/train", |
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"dev": "SciDTB-master/dataset/dev/gold", |
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"test": "SciDTB-master/dataset/test/gold", |
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}, |
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encoding="utf-8-sig", |
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), |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"root": datasets.Sequence( |
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{ |
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"id": datasets.Value("int32"), |
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"parent": datasets.Value("int32"), |
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"text": datasets.Value("string"), |
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"relation": datasets.Value("string"), |
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} |
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), |
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"file_name": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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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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data_dir = dl_manager.download_and_extract(_URL) |
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return [ |
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SplitGenerator( |
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name=split, |
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gen_kwargs={ |
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"dir_path": os.path.join(data_dir, subdir), |
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}, |
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) |
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for split, subdir in self.config.subdirectory_mapping.items() |
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] |
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def _generate_examples(self, dir_path): |
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_files = glob.glob(f"{dir_path}/*.dep") |
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for file_path in _files: |
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with open(file_path, mode="r", encoding=self.config.encoding) as f: |
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annotations = json.load(f) |
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annotations["file_name"] = os.path.basename(file_path) |
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yield annotations["file_name"], annotations |
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