# Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain

arXiv: https://arxiv.org/abs/2112.05807 · HTML: https://mlsanity.com/paper/2112.05807/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks](https://mlsanity.com/paper/1807.07425/) (2018)

## Built on by (most cited first, 12 of 12)

- [Unlocking Practical Applications in Legal Domain: Evaluation of GPT for Zero-Shot Semantic Annotation of Legal Texts](https://mlsanity.com/paper/2305.04417/) (2023)
- [Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?](https://mlsanity.com/paper/2306.13906/) (2023)
- [Using Large Language Models to Support Thematic Analysis in Empirical Legal Studies](https://mlsanity.com/paper/2310.18729/) (2023)
- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents](https://mlsanity.com/paper/2112.11494/) (2021)
- [Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization](https://mlsanity.com/paper/2502.20364/) (2025)
- [LLMediator: GPT-4 Assisted Online Dispute Resolution](https://mlsanity.com/paper/2307.16732/) (2023)
- [Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data](https://mlsanity.com/paper/2112.07870/) (2021)
- [Toward an Intelligent Tutoring System for Argument Mining in Legal Texts](https://mlsanity.com/paper/2210.13635/) (2022)
- [Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?](https://mlsanity.com/paper/2303.09325/) (2023)
- [Automated Creation of the Legal Knowledge Graph Addressing Legislation on Violence Against Women: Resource, Methodology and Lessons Learned](https://mlsanity.com/paper/2508.06368/) (2025)

## Abstract (arXiv)

In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer Assisted Semantic Exploration) which exploits word co-occurrence to guide human annotators in selection of relevant search terms. The system seamlessly facilitates iterative evaluation and improvement of the classification rules. The process enables the human annotators to leverage the benefits of statistical information while incorporating their expert intuition into the creation of such rules. We evaluate classifiers created with our CASE system on 4 datasets, and compare the results to machine learning methods, including SKOPE rules, Random forest, Support Vector Machine, and fastText classifiers. The results drive the discussion on trade-offs between superior compactness, simplicity, and intuitiveness of the Boolean search rules versus the better performance of state-of-the-art machine learning models for text classification.