Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K - 1M
License:
metadata
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: coarse-discourse
pretty_name: Coarse Discourse
dataset_info:
features:
- name: title
dtype: string
- name: is_self_post
dtype: bool
- name: subreddit
dtype: string
- name: url
dtype: string
- name: majority_link
dtype: string
- name: is_first_post
dtype: bool
- name: majority_type
dtype: string
- name: id_post
dtype: string
- name: post_depth
dtype: int32
- name: in_reply_to
dtype: string
- name: annotations
sequence:
- name: annotator
dtype: string
- name: link_to_post
dtype: string
- name: main_type
dtype: string
splits:
- name: train
num_bytes: 45097556
num_examples: 116357
download_size: 4256575
dataset_size: 45097556
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Card for "coarse_discourse"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage:
- Repository: https://github.com/google-research-datasets/coarse-discourse
- Paper: Characterizing Online Discussion Using Coarse Discourse Sequences
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 4.63 MB
- Size of the generated dataset: 45.45 MB
- Total amount of disk used: 50.08 MB
Dataset Summary
A large corpus of discourse annotations and relations on ~10K forum threads.
We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
default
- Size of downloaded dataset files: 4.63 MB
- Size of the generated dataset: 45.45 MB
- Total amount of disk used: 50.08 MB
An example of 'train' looks as follows.
{
"annotations": {
"annotator": ["fc96a15ab87f02dd1998ff55a64f6478", "e9e4b3ab355135fa954badcc06bfccc6", "31ac59c1734c1547d4d0723ff254c247"],
"link_to_post": ["", "", ""],
"main_type": ["elaboration", "elaboration", "elaboration"]
},
"id_post": "t1_c9b30i1",
"in_reply_to": "t1_c9b2nyd",
"is_first_post": false,
"is_self_post": true,
"majority_link": "t1_c9b2nyd",
"majority_type": "elaboration",
"post_depth": 2,
"subreddit": "100movies365days",
"title": "DTX120: #87 - Nashville",
"url": "https://www.reddit.com/r/100movies365days/comments/1bx6qw/dtx120_87_nashville/"
}
Data Fields
The data fields are the same among all splits.
default
title
: astring
feature.is_self_post
: abool
feature.subreddit
: astring
feature.url
: astring
feature.majority_link
: astring
feature.is_first_post
: abool
feature.majority_type
: astring
feature.id_post
: astring
feature.post_depth
: aint32
feature.in_reply_to
: astring
feature.annotations
: a dictionary feature containing:annotator
: astring
feature.link_to_post
: astring
feature.main_type
: astring
feature.
Data Splits
name | train |
---|---|
default | 116357 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@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} }
Contributions
Thanks to @thomwolf, @lewtun, @jplu for adding this dataset.