README.md

metadata

annotations_creators:
  - found
language:
  - en
language_creators:
  - found
license:
  - unknown
multilinguality:
  - monolingual
pretty_name: GuardianAuthorship
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
  - topic-classification
paperswithcode_id: null

Dataset Card for "guardian_authorship"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • 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
  • Contributions

Dataset Description

• Homepage: http://www.icsd.aegean.gr/lecturers/stamatatos/papers/JLP2013.pdf
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 47.31 MB
• Size of the generated dataset: 37.17 MB
• Total amount of disk used: 84.49 MB

Dataset Summary

A dataset cross-topic authorship attribution. The dataset is provided by Stamatatos 2013. 1- The cross-topic scenarios are based on Table-4 in Stamatatos 2017 (Ex. cross_topic_1 => row 1:P S U&W ). 2- The cross-genre scenarios are based on Table-5 in the same paper. (Ex. cross_genre_1 => row 1:B P S&U&W).

3- The same-topic/genre scenario is created by grouping all the datasts as follows. For ex., to use same_topic and split the data 60-40 use: train_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[:60%]+validation[:60%]+test[:60%]') tests_ds = load_dataset('guardian_authorship', name="cross_topic_<<#>>", split='train[-40%:]+validation[-40%:]+test[-40%:]')

IMPORTANT: train+validation+test[:60%] will generate the wrong splits because the data is imbalanced

• See https://huggingface.co/docs/datasets/splits.html for detailed/more examples

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

cross_genre_1

• Size of downloaded dataset files: 2.96 MB
• Size of the generated dataset: 2.61 MB
• Total amount of disk used: 5.57 MB

An example of 'train' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 4
}

cross_genre_2

• Size of downloaded dataset files: 2.96 MB
• Size of the generated dataset: 2.61 MB
• Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 1
}

cross_genre_3

• Size of downloaded dataset files: 2.96 MB
• Size of the generated dataset: 2.61 MB
• Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 2
}

cross_genre_4

• Size of downloaded dataset files: 2.96 MB
• Size of the generated dataset: 2.61 MB
• Total amount of disk used: 5.57 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 3
}

cross_topic_1

• Size of downloaded dataset files: 2.96 MB
• Size of the generated dataset: 2.23 MB
• Total amount of disk used: 5.18 MB

An example of 'validation' looks as follows.

{
    "article": "File 1a\n",
    "author": 0,
    "topic": 1
}

Data Fields

The data fields are the same among all splits.

cross_genre_1

• author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
• topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
• article: a string feature.

cross_genre_2

• author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
• topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
• article: a string feature.

cross_genre_3

• author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
• topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
• article: a string feature.

cross_genre_4

• author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
• topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
• article: a string feature.

cross_topic_1

• author: a classification label, with possible values including catherinebennett (0), georgemonbiot (1), hugoyoung (2), jonathanfreedland (3), martinkettle (4).
• topic: a classification label, with possible values including Politics (0), Society (1), UK (2), World (3), Books (4).
• article: a string feature.

Data Splits

name	train	validation	test
cross_genre_1	63	112	269
cross_genre_2	63	62	319
cross_genre_3	63	90	291
cross_genre_4	63	117	264
cross_topic_1	112	62	207

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@article{article,
    author = {Stamatatos, Efstathios},
    year = {2013},
    month = {01},
    pages = {421-439},
    title = {On the robustness of authorship attribution based on character n-gram features},
    volume = {21},
    journal = {Journal of Law and Policy}
}

@inproceedings{stamatatos2017authorship,
    title={Authorship attribution using text distortion},
    author={Stamatatos, Efstathios},
    booktitle={Proc. of the 15th Conf. of the European Chapter of the Association for Computational Linguistics},
    volume={1}
    pages={1138--1149},
    year={2017}
}

Contributions

Thanks to @thomwolf, @eltoto1219, @malikaltakrori for adding this dataset.