<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The spread of online databases and the increasingly sophisticated search solutions in the past 10–15 years have opened up many new opportunities for lawyers to find relevant documents. However, it is still a common problem that the various legal databases and legal search engines face an information crisis. Legal database providers use various information extraction solutions, especially named entity recognition (NER), to mitigate this problem. These solutions can improve the relevance of the lists of results. Their limitation, however, is that they can only extract and create searchable metadata entities if the latter have a well-defined location or regularity in the text. Therefore, the next era of search support for legal databases is semantic processing. Semantic processing solutions are fundamentally different from information extraction and NER because they do not only extract and make visible and/or searchable the specific information element contained in the text but allow for the analytical analysis of the text as a whole. In addition, in many cases, legal database developments using machine learning can be a significant burden on a company, as it is not always known what kind of an AI solution is needed, and how the providers could compare the different solutions. Legal database providers need to customize processing their documents and texts in the most optimal way possible, considering all their legal, linguistic, statistical, or other characteristics. This is where text processing pipelines can help. So, the article reviews the two main natural language processing (NLP) solutions which can help legal database providers to increase the value of legal data within legal databases. The article then shows the importance of text-processing pipelines and frameworks in the era of digitized documents and presents the digital-twin-distiller.</p>
</abstract>

The spread of online databases and the increasingly sophisticated search solutions in the past 10–15 years have opened up many new opportunities for lawyers to find relevant documents. However, it is still a common problem that the various legal databases and legal search engines face an information crisis. Legal database providers use various information extraction solutions, especially named entity recognition (NER), to mitigate this problem. These solutions can improve the relevance of the lists of results. Their limitation, however, is that they can only extract and create searchable metadata entities if the latter have a well-defined location or regularity in the text. Therefore, the next era of search support for legal databases is semantic processing. Semantic processing solutions are fundamentally different from information extraction and NER because they do not only extract and make visible and/or searchable the specific information element contained in the text but allow for the analytical analysis of the text as a whole. In addition, in many cases, legal database developments using machine learning can be a significant burden on a company, as it is not always known what kind of an AI solution is needed, and how the providers could compare the different solutions. Legal database providers need to customize processing their documents and texts in the most optimal way possible, considering all their legal, linguistic, statistical, or other characteristics. This is where text processing pipelines can help. So, the article reviews the two main natural language processing (NLP) solutions which can help legal database providers to increase the value of legal data within legal databases. The article then shows the importance of text-processing pipelines and frameworks in the era of digitized documents and presents the digital-twin-distiller.

How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases?

Doctoral School of Law, Eötvös Loránd University

TalTech Journal of European Studies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The spread of online databases and the increasingly sophisticated search solutions in the past 10–15 years have opened up many new opportunities for lawyers to...

How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases?

Publicado en línea: 09 dic 2023

Páginas: 138 - 151

DOI: https://doi.org/10.2478/bjes-2023-0018

Palabras clave
digital-twin-distiller, legal databases, legal research, named entity recognition, semantic processing, text-processing pipelines

© 2023 Renátó Vági, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

How Could Semantic Processing and Other NLP Tools Improve Online Legal Databases?

Publicado en línea: 09 dic 2023

Páginas: 138 - 151

DOI: https://doi.org/10.2478/bjes-2023-0018

Palabras clavedigital-twin-distiller, legal databases, legal research, named entity recognition, semantic processing, text-processing pipelines

© 2023 Renátó Vági, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
digital-twin-distiller, legal databases, legal research, named entity recognition, semantic processing, text-processing pipelines