Design and Implementation of a Systematic Catalog of Natural Language Processing Use Cases in the Legal Domain

Abstract

The field of Legal Tech has been increasingly benefiting from Natural Language Processing (NLP) technologies. However, there has been little systematic research on building a joint knowledge base for NLP technologies and Legal Tech. While there are several public Tech Maps for Legal Tech, they all focus on Legal Tech companies instead of the use cases.

The proposed research topic aims to produce a tech map that maps state-of-the-art NLP techniques to their application use cases. This will provide a comprehensive and intuitive approach to explore NLP techniques, Legal Tech use cases and their associations.

The research will be conducted based on the results of the previous research stage, which provides a set of structured use cases of NLP technologies in the legal domain. The student will first validate the use cases via interviews or surveys. They will identify the entities and data structures needed to create the tech map and further design and implement it. Final evaluation will be carried out with stakeholders including NLP technologists and law experts.

This research will deliver the first project deliverable of the NLawP project and fill in the research gaps of a joint knowledge base for NLP and Legal Tech. Further analysis and research could be conducted based on this Tech Map.

Research Questions

1. How can Legal Tech use cases from previous research be validated?
2. How can one structure the use cases into a joint knowledge base with bidirectional relations, and from an engineering perspective, what entities are needed to implement such a joint knowledge base?
3. What is required to create a user-friendly and intuitive tech map, which facilitates indexing, exploring, searching and navigating among the knowledge base?
4. How can the usability of the tech map be evaluated, e.g., with Technology Acceptance Model (TAM), especially for legal practitioners?

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