Search

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.

This research paper presents findings from an investigation in the semantic similarity search task within the legal domain, using a corpus of 1172 Hungarian court decisions. The study establishes the groundwork for an operational semantic similarity search system designed to identify cases with comparable facts using preliminary legal fact drafts. Evaluating such systems often poses significant challenges, given the need for thorough document checks, which can be costly and limit evaluation reusability. To address this, the study employs manually created fact drafts for legal cases, enabling reliable ranking of original cases within retrieved documents and quantitative comparison of various vectorization methods. The study compares twelve different text embedding solutions (the most recent became available just a few weeks before the manuscript was written) identifying Cohere's embed-multilingual-v3.0, Beijing Academy of Artificial Intelligence's bge-m3, Jina AI's jina-embeddings-v3, OpenAI's text-embedding-3-large, and Microsoft's multilingual-e5-large models as top performers. To overcome the transformer-based models' context window limitation, we investigated chunking, striding, and last chunk scaling techniques, with last chunk scaling significantly improving embedding quality. The results suggest that the effectiveness of striding varies based on token count. Notably, employing striding with 16 tokens yielded optimal results, representing 3.125% of the context window size for the best-performing models. Results also suggested that from the models having 8192 token long context window the bge-m3 model is superior compared to jina-embeddings-v3 and text-embedding-3-large models in capturing the relevant parts of a document if the text contains significant amount of noise. The validity of the approach was evaluated and confirmed by legal experts. These insights led to an operational semantic search system for a prominent legal content provider. [ABSTRACT FROM AUTHOR]

Few-shot learning is a deep learning subfield that is the focus of research nowadays. This paper addresses the research question of whether a triplet-trained Siamese network, initially designed for multi-class classification, can effectively handle multi-label classification. We conducted a case study to identify any limitations in its application. The experiments were conducted on a dataset containing Hungarian legal decisions of administrative agencies in tax matters belonging to a major legal content provider. We also tested how different Siamese embeddings compare on classifying a previously non-existing label on a binary and a multi-label setting. We found that triplet-trained Siamese networks can be applied to perform classification but with a sampling restriction during training. We also found that the overlap between labels affects the results negatively. The few-shot model, seeing only ten examples for each label, provided competitive results compared to models trained on tens of thousands of court decisions using tf-idf vectorization and logistic regression. [ABSTRACT FROM AUTHOR]

Access to justice is a significant area of legal research, especially for Socio-Legal studies. The main research topics of this area are economic or class differences, gender inequalities, or national and ethnic differences in access to justice. However, there is a less discussed aspect of access to justice: the differences between access to legal information and the differences in user groups in terms of comprehending and processing legal information. This is an important topic because there are significant differences among people's abilities to process and understand legal texts, depending on whether we are dealing with a lawyer who is an expert in the given field, a non-expert lawyer, or a citizen with a low or zero (legal) educational level. The paper argues that unsupervised machine learning solutions can help even out these differences. It presents different unsupervised solutions, mainly clustering and topic modelling, which can help to increase access to legal information. Then we present a case study in which we examine these unsupervised tools in the processing of resolutions of the Central Bank in Hungary and anonymized court decisions. The paper argues that these tools can reveal the hidden contextual regularities in unstructured legal texts, facilitating the search for legal texts even for non-legal-experts. [ABSTRACT FROM AUTHOR]

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company's structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process. [ABSTRACT FROM AUTHOR]