Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 5,398 Bytes
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"""TODO(coarse_discourse): Add a description here."""
import json
import os
import datasets
# TODO(coarse_discourse): BibTeX citation
_CITATION = """\
@inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} }
"""
# TODO(coarse_discourse):
_DESCRIPTION = """\
dataset contains discourse annotation and relation on threads from reddit during 2016
"""
_URL = "https://github.com/google-research-datasets/coarse-discourse/archive/master.zip"
class CoarseDiscourse(datasets.GeneratorBasedBuilder):
"""TODO(coarse_discourse): Short description of my dataset."""
# TODO(coarse_discourse): Set up version.
VERSION = datasets.Version("0.1.0")
def _info(self):
# TODO(coarse_discourse): Specifies the datasets.DatasetInfo object
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
# These are the features of your dataset like images, labels ...
"title": datasets.Value("string"),
"is_self_post": datasets.Value("bool"),
"subreddit": datasets.Value("string"),
"url": datasets.Value("string"),
"majority_link": datasets.Value("string"),
"is_first_post": datasets.Value("bool"),
"majority_type": datasets.Value("string"),
"id_post": datasets.Value("string"),
"post_depth": datasets.Value("int32"),
"in_reply_to": datasets.Value("string"),
"annotations": datasets.features.Sequence(
{
"annotator": datasets.Value("string"),
"link_to_post": datasets.Value("string"),
"main_type": datasets.Value("string"),
}
),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage="https://github.com/google-research-datasets/coarse-discourse",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# TODO(coarse_discourse): Downloads the data and defines the splits
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLs
dl_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(dl_dir, "coarse-discourse-master", "coarse_discourse_dataset.json")
},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
# TODO(coarse_discourse): Yields (key, example) tuples from the dataset
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
url = data.get("url", "")
is_self_post = data.get("is_self_post", "")
subreddit = data.get("subreddit", "")
title = data.get("title", "")
posts = data.get("posts", "")
for id1, post in enumerate(posts):
maj_link = post.get("majority_link", "")
maj_type = post.get("majority_type", "")
id_post = post.get("id", "")
is_first_post = post.get("is_firs_post", "")
post_depth = post.get("post_depth", -1)
in_reply_to = post.get("in_reply_to", "")
annotations = post["annotations"]
annotators = [annotation.get("annotator", "") for annotation in annotations]
main_types = [annotation.get("main_type", "") for annotation in annotations]
link_posts = [annotation.get("linkk_to_post", "") for annotation in annotations]
yield str(id_) + "_" + str(id1), {
"title": title,
"is_self_post": is_self_post,
"subreddit": subreddit,
"url": url,
"majority_link": maj_link,
"is_first_post": is_first_post,
"majority_type": maj_type,
"id_post": id_post,
"post_depth": post_depth,
"in_reply_to": in_reply_to,
"annotations": {"annotator": annotators, "link_to_post": link_posts, "main_type": main_types},
}
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