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

• URL: http://arxiv.org/abs/2112.05807v1
• Date: Fri, 10 Dec 2021 19:53:41 GMT
• Title: Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain
• Authors: Hannes Westermann, Jaromir Savelka, Vern R. Walker, Kevin D. Ashley, Karim Benyekhlef
• Abstract summary: We develop an interactive environment called CASE 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. We evaluate classifiers created with our CASE system on 4 datasets, and compare the results to machine learning methods.
• Score: 0.5249805590164901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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