Special Issue "Information Technology and Emerging Legal Informatics (Legomatics)"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (30 October 2021).

Special Issue Editors

Dr. Sugam Sharma
E-Mail Website
Guest Editor
1. Center for Survey Statistics and Methodology (CSSM), Iowa State University, Ames, IA 50011-1210, USA
2. eFeed-Hungers.com, Ames, IA 50014, USA
Interests: data-driven science; big data; spatial data; GIS; smart home technology; health informatics; social and legal informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Richard Lomotey
E-Mail Website
Guest Editor
College of Information Sciences and Technology, Pennsylvania State University, Monaca, PA 15061, USA
Interests: mobile cloud computing; enterprise applications; cyber physical devices or Internet of Things (IoT); web services; databases (RDBMs and NoSQL)

Special Issue Information

Dear Colleagues, 

The evolution of Computer Science (CS) and Information Technology (IT) has helped to advance several other fields, and lately, it has begun to expand into the domain of Law as well, where their blending and cohesiveness is being dubbed Legal Informatics (LI). Currently, LI is rapidly emerging as a new, interesting research area at the confluence of CS, IT, and Law. More recently, LI has drawn attention from the computational and legal communities because of the growing popularity, increasing acceptability, broadening usage, and extensive benefits of Artificial Intelligence (AI) and its allied techniques and technologies, such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). The goal is to apply and utilize these modern computational technologies—AI, ML, DL, NLP—to advance and improve the existing jurisprudence/legal procedure/legal justice system/civil justice system. More and more legal scholars and practitioners and computer and data scientists and engineers are teaming up to combine their expertise and knowledge to transform the existing jurisprudence into a smart and intelligent justice system, which is faster, fair, and economically feasible for every last person of marginalized and underprivileged societies, “the poor”. This Special Issue (SI) invites original, unpublished scholarly work related to information technology applied in emerging legal informatics and other areas. 

The topics of interest include but are not limited to:

  • Algorithms and applications;
  • AI, ML, and DL in jurisprudence;
  • NLP and text processing;
  • Ontology and knowledge representation;
  • Legal data;
  • Legal information retrieval;
  • Blockchain technology;
  • Learning deep from legal data;
  • Data visualization and analyses;
  • Smart contracts;
  • Legal expert systems;
  • Legal predictive systems;
  • Robotics;
  • Automated dispute resolution systems.

Dr. Sugam Sharma
Dr. Richard Lomotey
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Automatic Curation of Court Documents: Anonymizing Personal Data
Information 2022, 13(1), 27; https://0-doi-org.brum.beds.ac.uk/10.3390/info13010027 - 10 Jan 2022
Viewed by 116
In order to provide open access to data of public interest, it is often necessary to perform several data curation processes. In some cases, such as biological databases, curation involves quality control to ensure reliable experimental support for biological sequence data. In others, [...] Read more.
In order to provide open access to data of public interest, it is often necessary to perform several data curation processes. In some cases, such as biological databases, curation involves quality control to ensure reliable experimental support for biological sequence data. In others, such as medical records or judicial files, publication must not interfere with the right to privacy of the persons involved. There are also interventions in the published data with the aim of generating metadata that enable a better experience of querying and navigation. In all cases, the curation process constitutes a bottleneck that slows down general access to the data, so it is of great interest to have automatic or semi-automatic curation processes. In this paper, we present a solution aimed at the automatic curation of our National Jurisprudence Database, with special focus on the process of the anonymization of personal information. The anonymization process aims to hide the names of the participants involved in a lawsuit without losing the meaning of the narrative of facts. In order to achieve this goal, we need, not only to recognize person names but also resolve co-references in order to assign the same label to all mentions of the same person. Our corpus has significant differences in the spelling of person names, so it was clear from the beginning that pre-existing tools would not be able to reach a good performance. The challenge was to find a good way of injecting specialized knowledge about person names syntax while taking profit of previous capabilities of pre-trained tools. We fine-tuned an NER analyzer and we built a clusterization algorithm to solve co-references between named entities. We present our first results, which, for both tasks, are promising: We obtained a 90.21% of F1-micro in the NER task—from a 39.99% score before retraining the same analyzer in our corpus—and a 95.95% ARI score in clustering for co-reference resolution. Full article
(This article belongs to the Special Issue Information Technology and Emerging Legal Informatics (Legomatics))
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