KEVLAR: the Complete Resource for EuroVoc Classification of Legal Documents
Authors: Lorenzo Bocchi, Camilla Casula, Alessio Palmero Aprosio (Università di Trento, Fondazione Bruno Kessler, 2024)
Topic: In-domain IR and IE

Abstract
The use of Machine Learning and Artificial Intelligence in the Public Administration (PA) has increased in the last years. In particular, recent guidelines proposed by various governments for the classification of documents released by the PA suggest to use the EuroVoc thesaurus. In this paper, we present KEVLAR, an all-in-one solution for performing the above-mentioned task on acts belonging to the Public Administration. First, we create a collection of 8 million documents in 24 languages, tagged with EuroVoc labels, taken from EUR-Lex, the web portal of the European Union legislation. Then, we train different pre-trained BERT-based models, comparing the performance of base models with domain-specific and multilingual ones. We release the corpus, the best-performing models, and a Docker image containing the source code of the trainer, the REST API, and the web interface. This image can be employed out-of-the-box for document classification.

Introduction
EuroVoc is a multilingual and multidisciplinary thesaurus that has seen a significant rise in its use and importance in recent years. In particular, the taxonomy used in this thesaurus has become crucial for a number of activities of European Public Administrations, shaping the way information is organized, disseminated, and accessed. Containing over 7,000 concepts, EuroVoc acts as a reliable and efficient indexing system for a vast range of documents, legislative texts, and reports. Due to this, a growing number of governmental institutions around Europe has begun to use it internally for document categorization. The Spanish government, for instance, has suggested the adoption of EuroVoc since 2014 AUTHOR, and has more recently started using it regularly in its official open data portal,} and in the Portal de la Administración Electrónica website. Similarly, German and French public administrations are following the same strategy, in the DCAT-AP.de} and data.gouv.fr} portals respectively. Furthermore, \citet{rovera2023} presented a preliminary study that explores the migration of the Gazzetta Ufficiale, the official journal of records of the Italian government, towards the adoption of the EuroVoc taxonomy. Similar initiatives have also grown in other European countries AUTHOR. \centering \includegraphics[width=\linewidth]{demo.png In this paper we present KEVLAR, Kessler EuroVoc Laws and Acts Repository, which aims at fulfilling a number of purposes. \item First, we release a collection of more than 8 million documents from EUR-Lex, the European Union's official web portal, which gives comprehensive access to EU legal documents, spanning more than 70 years of EU legislation (1948-2022), and covering 24 languages. Over half of these texts are already tagged with the corresponding EuroVoc concepts. \item Secondly, we perform a series of experiments for automatic tagging of the documents using the EuroVoc taxonomy, comparing different approaches and language models. \item Finally, we develop a web interface (see Figure~demo) and a REST API that anyone (citizen or public administration) could use both to easily try automatic classification of documents and to integrate such categorization in any systems that might need it. The models used for the web demo and the release are the best-performing ones we found, as described in Section~sec:discussion. All the data and tools (the set of documents labeled with EuroVoc labels, the models, and the demo code) are freely available for download.

Conclusion
In this paper, we release KEVLAR, an all-in-one solution for performing the document classification task on acts belonging to the Public Administration. We collected more than 8 million documents in 24 languages, compared different BERT and RoBERTa-based models on the classification of documents with respect to the EuroVoc taxonomy, and built an out-of-the-box tool for easily applying the classification to any text. In the future, we will continue the exploration of novel methods to address this task with potentially better performance, for example using better-performing models or exploiting generation-based solutions.