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""" |
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Arguement Mining Dataset created by Stab , Gurevych et. al. CL 2017 |
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""" |
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import datasets |
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import os |
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_CITATION = """\ |
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@article{stab2017parsing, |
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title={Parsing argumentation structures in persuasive essays}, |
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author={Stab, Christian and Gurevych, Iryna}, |
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journal={Computational Linguistics}, |
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volume={43}, |
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number={3}, |
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pages={619--659}, |
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year={2017}, |
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publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…} |
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} |
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""" |
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_DESCRIPTION = """\ |
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tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement |
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denoted by I-ARG, other chunks are O |
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Orginial train,test split as used by the paper is provided |
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""" |
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_URL = "https://raw.githubusercontent.com/Sam131112/Argument-Mining-Dataset/main/" |
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_TRAINING_FILE = "train.txt" |
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_TEST_FILE = "test.txt" |
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class ArguementMiningCL2017Config(datasets.BuilderConfig): |
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"""BuilderConfig for CL2017""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig forCl2017. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(ArguementMiningCL2017Config, self).__init__(**kwargs) |
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class ArguementMiningCL2017(datasets.GeneratorBasedBuilder): |
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"""CL2017 dataset.""" |
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BUILDER_CONFIGS = [ |
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ArguementMiningCL2017Config(name="cl2017", version=datasets.Version("1.0.0"), description="Cl2017 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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"chunk_tags":datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-ARG", |
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"I-ARG", |
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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://direct.mit.edu/coli/article/43/3/619/1573/Parsing-Argumentation-Structures-in-Persuasive", |
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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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urls_to_download = { |
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"train": _TRAINING_FILE, |
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"test": _TEST_FILE, |
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} |
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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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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print("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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guid = 0 |
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tokens = [] |
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pos_tags = [] |
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chunk_tags = [] |
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ner_tags = [] |
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for line in f: |
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if line == "\n": |
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if tokens: |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"chunk_tags": chunk_tags, |
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} |
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guid = guid+1 |
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tokens = [] |
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chunk_tags = [] |
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else: |
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line=line.strip('\n') |
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splits = line.split("\t") |
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tokens.append(splits[0]) |
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chunk_tags.append(splits[1]) |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"chunk_tags": chunk_tags, |
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} |
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