Automatic Detection of Industry Sectors in Legal Articles Using Machine Learning Approaches

TL;DR

This paper presents a machine learning approach combining NLP and statistical techniques to automatically identify industry sectors in legal articles, enabling targeted legal news dissemination and industry analysis.

Contribution

It introduces a novel ML-based system for industry classification in legal texts, comparing traditional ML and deep learning methods on a newly created dataset.

Findings

Achieved AUC scores above 0.90 and F-scores above 0.81.

Traditional ML methods outperform deep neural networks with limited domain-specific data.

System enables scalable and efficient processing of legal articles for industry classification.

Abstract

The ability to automatically identify industry sector coverage in articles on legal developments, or any kind of news articles for that matter, can bring plentiful of benefits both to the readers and the content creators themselves. By having articles tagged based on industry coverage, readers from all around the world would be able to get to legal news that are specific to their region and professional industry. Simultaneously, writers would benefit from understanding which industries potentially lack coverage or which industries readers are currently mostly interested in and thus, they would focus their writing efforts towards more inclusive and relevant legal news coverage. In this paper, a Machine Learning-powered industry analysis approach which combined Natural Language Processing (NLP) with Statistical and Machine Learning (ML) techniques was investigated. A dataset consisting of over 1,700 annotated legal articles was created for the identification of six industry sectors. Text and legal based features were extracted from the text. Both traditional ML methods (e.g. gradient boosting machine algorithms, and decision-tree based algorithms) and deep neural network (e.g. transformer models) were applied for performance comparison of predictive models. The system achieved promising results with area under the receiver operating characteristic curve scores above 0.90 and F-scores above 0.81 with respect to the six industry sectors. The experimental results show that the suggested automated industry analysis which employs ML techniques allows the processing of large collections of text data in an easy, efficient, and scalable way. Traditional ML methods perform better than deep neural networks when only a small and domain-specific training data is available for the study.