nlpcc_stance.py

# Copyright 2022 Mads Kongsbak and Leon Derczynski
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""NLPCC Shared Task 4, Stance Detection in Chinese Microblogs (Task A)"""


import csv
import json
import os

import datasets

_CITATION = """\
@incollection{xu2016overview,
  title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
  author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
  booktitle={Natural language understanding and intelligent applications},
  pages={907--916},
  year={2016},
  publisher={Springer}
}
"""

_DESCRIPTION = """\
This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data. 
"""

_HOMEPAGE = "http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html"

_LICENSE = "cc-by-4.0"

class NLPCCConfig(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super(NLPCCConfig, self).__init__(**kwargs)

class NLPCCStance(datasets.GeneratorBasedBuilder):
    """The NLPCC Shared Task 4 dataset regarding Stance Detection in Chinese Microblogs (Task A)"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        NLPCCConfig(name="task_a", version=VERSION, description="Task A, the supervised learning task."),
    ]
    
    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "target": datasets.Value("string"),
                "text": datasets.Value("string"),
                "stance": datasets.features.ClassLabel(
                    names=[
                        "AGAINST",
                        "FAVOR",
                        "NONE",
                    ]
                )
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_text = dl_manager.download_and_extract("taska_train.csv")
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter=",")
            guid = 0
            for instance in reader:
                instance["target"] = instance.pop("target")
                instance["text"] = instance.pop("text")
                instance["stance"] = instance.pop("stance")
                instance['id'] = str(guid)
                yield guid, instance
                guid += 1