# Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains

arXiv: https://arxiv.org/abs/2010.09459 · HTML: https://mlsanity.com/paper/2010.09459/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements](https://mlsanity.com/paper/2402.10614/) (2024)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [RuleRAG: Rule-Guided Retrieval-Augmented Generation with Language Models for Question Answering](https://mlsanity.com/paper/2410.22353/) (2024)
- [GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns](https://mlsanity.com/paper/2104.03958/) (2021)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
- [Topic Ontologies for Arguments](https://mlsanity.com/paper/2301.09759/) (2023)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)
- [WIBA: What Is Being Argued? A Comprehensive Approach to Argument Mining](https://mlsanity.com/paper/2405.00828/) (2024)
- [Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis](https://mlsanity.com/paper/2409.17417/) (2024)
- [An LLM-Based System for Argument Mining](https://mlsanity.com/paper/2605.13793/) (2026)

## Abstract (arXiv)

Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain experts in their first steps into a new task over a new corpus, we present an unsupervised approach to reveal complex rules which cluster the unexplored corpus by its prominent categories (or facets). These rules are human-readable, thus providing an important ingredient which has become in short supply lately - explainability. Each rule provides an explanation for the commonality of all the texts it clusters together. We present an extensive evaluation of the usefulness of these rules in identifying target categories, as well as a user study which assesses their interpretability.