A visual search engine for Bangladeshi laws
This addresses the problem of inefficient access to legal information for law students, researchers, and citizens in Bangladesh, though it is incremental as it applies existing methods to a new domain.
The paper tackles the challenge of browsing and finding relevant information in Bangladeshi laws by developing a visual search engine that uses machine learning techniques, resulting in a faster and better search experience based on qualitative feedback from users.
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.