## 1 Introduction  
Recently, we have witnessed the application of Artificial Intelligence (AI) in different domains, such as health, computer vision, privacy and law. This trend was due to the increasing data gathered worldwide. This amount of rich data allows the use of AI-based techniques. Particularly, we have seen different efforts to collect and make available legal corpora in the law domain, such as terms-of-service agreements [1], privacy policies (Privacy policies [[https://usableprivacy.org/data](https://usableprivacy.org/data)]([https://usableprivacy.org/data](https://usableprivacy.org/data)), accessed on 13 March 2022), English contracts manually annotated [2], legislative documents from The EU's public document database [3], sentences from German rental agreements [4], court decisions on cybercrime and trade secrets [5], as well as Australian legal cases from the Federal Court of Australia (FCA [[https://archive.ics.uci.edu/ml/datasets/Legal+Case+Reports#](https://archive.ics.uci.edu/ml/datasets/Legal+Case+Reports#)]([https://archive.ics.uci.edu/ml/datasets/Legal+Case+Reports#](https://archive.ics.uci.edu/ml/datasets/Legal+Case+Reports#)), accessed on 13 March 2022), Danish Legal monolingual corpus (Danish Legal corpus elrc-share.eu/repository/browse/danish-legal-monolingual-corpus-from-the-contents-of-the-retsinformationdk-web-site, accessed on 20 March 2022), Japanese-English Legal Parallel Corpus (Japanese-English Legal Parallel Corpus [[https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus](https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus)]([https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus](https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus)), accessed on 15 March 2022), Spanish Legal monolingual corpus (Danish Legal corpus elrc-share.eu/repository/browse/danish-legal-monolingual-corpus-from-the-contents-of-the-retsinformationdk-web-site, accessed on 20 March 2022), Japanese-English Legal Parallel Corpus (Japanese-English Legal Parallel Corpus [[https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus](https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus)]([https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus](https://datarepository.wolframcloud.com/resources/Japanese-English-Legal-Parallel-Corpus)), accessed on 15 March 2022),  
March 2022), and British and Irish Legal documents (British and Irish Legal Information Institute [[https://www.bailli.org/](https://www.bailli.org/)]([https://www.bailli.org/](https://www.bailli.org/)), accessed on 20 March 2022) and Human rights (British and Irish Legal, 2022). These sources of information enable the use of text mining to identify facts, relationships and assertions hidden in the vast amounts of textual data and documents. Text mining uses various methods to process text, the most important being Natural Language Processing (NLP). NLP, also known as computational linguistics, is a subfield of AI that learns, understands, recognizes, and produces human language content (Birtish and Irish, 2022). NLP techniques have been used in machine translation, word sense disambiguation, summarization, syntactic annotation and named entity recognition (Birtish and Irish, 2022). Specifically, NLP techniques have been used in different legal fields, demonstrating that AI can provide valuable support in legal work (Birtish and Irish, 2022; Birtish and Irish, 2022; Birtish and Irish, 2022). We mainly focused on Judgebot, which is the application of computational linguistic techniques for rule decision prediction in different legal domains, contexts, and languages. In the industry, applying AI to certain processes is more accurate, faster, and cheaper than using humans. Thus, if we transpose these benefits to the legal system, using AI could reduce the time and costs of justice administration.