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

Abstract

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.