# Legal Document Retrieval using Document Vector Embeddings and Deep Learning

arXiv: https://arxiv.org/abs/1805.10685 · HTML: https://mlsanity.com/paper/1805.10685/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2018.

## Built on by (most cited first, 13 of 13)

- [AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation](https://mlsanity.com/paper/2403.02959/) (2024)
- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks](https://mlsanity.com/paper/2301.12847/) (2023)
- [Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons](https://mlsanity.com/paper/2212.09080/) (2022)
- [VLQA: The First Comprehensive, Large, and High-Quality Vietnamese Dataset for Legal Question Answering](https://mlsanity.com/paper/2507.19995/) (2025)
- [Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network](https://mlsanity.com/paper/2105.06083/) (2021)
- [Transformer-based Approaches for Legal Text Processing](https://mlsanity.com/paper/2202.06397/) (2022)
- [Improving Vietnamese Legal Question--Answering System based on Automatic Data Enrichment](https://mlsanity.com/paper/2306.04841/) (2023)
- [OPAD: An Optimized Policy-based Active Learning Framework for Document Content Analysis](https://mlsanity.com/paper/2110.02069/) (2021)
- [An interpretability framework for Similar case matching](https://mlsanity.com/paper/2304.01622/) (2023)
- [Application of Text Analytics in Public Service Co-Creation: Literature Review and Research Framework](https://mlsanity.com/paper/2305.18316/) (2023)
- [SHADE: Semantic Hypernym Annotator for Domain-specific Entities -- DnD Domain Use Case](https://mlsanity.com/paper/2407.00407/) (2024)
- [Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining](https://mlsanity.com/paper/2507.14619/) (2025)

## Abstract (arXiv)

Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.