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---
license: mit
size_categories:
- n<1K
task_categories:
- text-classification
pretty_name: liaisons IBM's Claim Stance Dataset Sample
dataset_info:
features:
- name: topic
dtype: string
- name: parent_argument
dtype: string
- name: child_argument
dtype: string
- name: relation
dtype: string
splits:
- name: binary
num_bytes: 21165
num_examples: 110
- name: ternary
num_bytes: 13861
num_examples: 72
download_size: 22681
dataset_size: 35026
configs:
- config_name: default
data_files:
- split: binary
path: data/binary-*
- split: ternary
path: data/ternary-*
tags:
- relation-based argument mining
- argument mining
- sample
---
---
⚠️ This repository is a part of an academical project for the Heriot-Watt University, no third-party contributions are accepted.
# Dataset Card for Liaison's IBM Claim Stance Dataset Sample
## Table of Contents
- [About the Dataset](#about-the-dataset)
- [About Contributions](#about-contributions)
- [Associated Works](#associated-works)
- [Licensing Information](#licensing-information)
- [Credits](#credits)
- [Special Thanks](#special-thanks)
## About the Dataset
### Dataset Summary
The present dataset is a result of processing the [IBM Debater Claim Stance Dataset](https://huggingface.co./datasets/ibm/claim_stance) to create representative samples. The size has been reduced to roughly 100 entries, enabling the benchmarking of models for relation-based argument mining tasks with limited resources.
You can also find here the associated benchmarking [framework](https://github.com/coding-kelps/liaisons-experiments) and [results](https://huggingface.co./datasets/coding-kelps/liaisons-experiments-results).
The sample also modifies the original dataset to achieve a more balanced plurality of stances and topics, and creates a new "unrelated" class in argument relation (following a simple rule-based data augmentation algorithm). Further details on the preprocessing can be found on [GitHub](https://github.com/coding-kelps/liaisons-preprocess).
### Dataset Structure
* parent_argument - The first argument that states a position regarding a topic
* child_argument - Another argument that is compared to the parent argument
* relation - The argumentative relation of the child argument to the parent argument. It can either be support/attack in the binary split or support/attack/unrelated in the ternary split
## About Contributions
As mentioned earlier, this work is part of an academic project for the validation of my Master's Degree at Heriot-Watt University, preventing me from accepting any contributions until the final release of my project. Thank you for your understanding.
## Associated Works
This work is part of a collection of works whose ultimate goal is to deliver a framework to automatically analyze social media content (e.g., X, Reddit) to extract their argumentative value and predict their relations, leveraging Large Language Models' (LLMs) abilities:
- [liaisons](https://github.com/coding-kelps/liaisons) (the developed client for social media content analysis)
- [liaisons-preprocess](https://github.com/coding-kelps/liaisons-preprocess) (the preprocessing of the original IBM dataset)
- [liaisons-experiments](https://github.com/coding-kelps/liaisons-experiments) (the benchmarking framework that the sample is intended to be used with)
- [liaisons-experiments-results](https://huggingface.co./datasets/coding-kelps/liaisons-experiments-results) (the obtained results with this benchmarking)
- [mantis-shrimp](https://github.com/coding-kelps/mantis-shrimp) (the configuration-as-code used to set up my workstation for this project)
## Licensing Information
This work includes data from the following sources:
* Wikipedia content licensed under CC BY-SA 3.0: [Wikipedia](https://en.wikipedia.org/wiki/Wikipedia:Copyrights#Reusers.27_rights_and_obligations)
* IBM content licensed under CC BY-SA 3.0: (c) Copyright IBM 2014. Released under [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/)
Modifications and preprocessing have been made to the original data. This derivative work is licensed under the same CC BY-SA 3.0 license.
## Credits
Further information about the original dataset can be found on its original [HuggingFace page](https://huggingface.co./datasets/ibm/claim_stance) and its associated research papers: [Stance Classification of Context-Dependent Claims](https://aclanthology.org/E17-1024/) and [Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization](https://aclanthology.org/W17-5104/).
## Special Thanks
I would like to credits [Andrew Ireland](http://www.macs.hw.ac.uk/~air/), my supervisor for this project.
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/0/03/Heriot-Watt_University_logo.svg/1200px-Heriot-Watt_University_logo.svg.png" width="300">
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