Datasets:

coding-kelps
/

liaisons-claim-stance-sample

@@ -43,24 +43,42 @@ tags:
 ## Table of Contents
-- [Dataset Summary](#dataset-summary)
-- [Dataset Structure](#dataset-structure)
 - [Licensing Information](#licensing-information)
-- [Citation Information](#citation-information)
-## 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
 ## Licensing Information
 This work includes data from the following sources:
@@ -70,6 +88,13 @@ This work includes data from the following sources:
 Modifications and preprocessing have been made to the original data. This derivative work is licensed under the same CC BY-SA 3.0 license.
-## Citation Information
 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/).

 ## 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:
 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">