A visual search engine for Bangladeshi laws

TL;DR

This paper introduces a machine learning-based visual search engine designed to improve access and navigation of digitized Bangladeshi legal documents, making legal research faster and more intuitive.

Contribution

It presents a novel visualization tool combining Doc2Vec, link mining, and named entity recognition tailored for Bangladeshi legal data, enhancing search efficiency.

Findings

Qualitative feedback indicates improved search experience.

The tool effectively visualizes citation networks.

Users find relevant legal sections more quickly.

Abstract

Browsing and finding relevant information for Bangladeshi laws is a challenge faced by all law students and researchers in Bangladesh, and by citizens who want to learn about any legal procedure. Some law archives in Bangladesh are digitized, but lack proper tools to organize the data meaningfully. We present a text visualization tool that utilizes machine learning techniques to make the searching of laws quicker and easier. Using Doc2Vec to layout law article nodes, link mining techniques to visualize relevant citation networks, and named entity recognition to quickly find relevant sections in long law articles, our tool provides a faster and better search experience to the users. Qualitative feedback from law researchers, students, and government officials show promise for visually intuitive search tools in the context of governmental, legal, and constitutional data in developing countries, where digitized data does not necessarily pave the way towards an easy access to information.