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

arXiv: https://arxiv.org/abs/2303.05387 · HTML: https://mlsanity.com/paper/2303.05387/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Exploring the Use of Text Classification in the Legal Domain](https://mlsanity.com/paper/1710.09306/) (2017)
- [Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1603.03827/) (2016)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [A Comparative Study of Transformer-Based Language Models on Extractive Question Answering](https://mlsanity.com/paper/2110.03142/) (2021)
- [Empirical Study of Deep Learning for Text Classification in Legal Document Review](https://mlsanity.com/paper/1904.01723/) (2019)
- [Optimal trees selection for classification via out-of-bag assessment and sub-bagging](https://mlsanity.com/paper/2012.15301/) (2020)

## Abstract (arXiv)

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.