Arguement_Mining_CL2017.py

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""
Arguement Mining Dataset created by Stab , Gurevych et. al. CL 2017 
"""

import datasets
import os


_CITATION = """\
@article{stab2017parsing,
  title={Parsing argumentation structures in persuasive essays},
  author={Stab, Christian and Gurevych, Iryna},
  journal={Computational Linguistics},
  volume={43},
  number={3},
  pages={619--659},
  year={2017},
  publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}
"""

_DESCRIPTION = """\
tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement
denoted by I-ARG, other chunks are O
Orginial train,test split as used by the paper is provided
"""

_URL = "https://raw.githubusercontent.com/Sam131112/Argument-Mining-Dataset/main/"
_TRAINING_FILE = "train.txt"
_TEST_FILE = "test.txt"


class ArguementMiningCL2017Config(datasets.BuilderConfig):
    """BuilderConfig for CL2017"""

    def __init__(self, **kwargs):
        """BuilderConfig forCl2017.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ArguementMiningCL2017Config, self).__init__(**kwargs)


class ArguementMiningCL2017(datasets.GeneratorBasedBuilder):
    """CL2017 dataset."""

    BUILDER_CONFIGS = [
        ArguementMiningCL2017Config(name="cl2017", version=datasets.Version("1.0.0"), description="Cl2017 dataset"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "chunk_tags":datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-ARG",
                                "I-ARG",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://direct.mit.edu/coli/article/43/3/619/1573/Parsing-Argumentation-Structures-in-Persuasive",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": _TRAINING_FILE,
            "test": _TEST_FILE,
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        print("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            pos_tags = []
            chunk_tags = []
            ner_tags = []
            for line in f:
                if line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "chunk_tags": chunk_tags,
                        }
                        guid = guid+1
                        tokens = []
                        chunk_tags = []
                else:
                    # cl2017 tokens are space separated
                    line=line.strip('\n')
                    splits = line.split("\t")
                    #print(splits)
                    tokens.append(splits[0])
                    chunk_tags.append(splits[1])
                    #print({"id": str(guid),"tokens": tokens,"chunk_tags": chunk_tags,})
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "chunk_tags": chunk_tags,
            }