# FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Search

arXiv: https://arxiv.org/abs/2601.17333 · HTML: https://mlsanity.com/paper/2601.17333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 24 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study](https://mlsanity.com/paper/2401.10825/) (2024)
- [Foundations of Vector Retrieval](https://mlsanity.com/paper/2401.09350/) (2024)
- [Towards a Natural Language Query Processing System](https://mlsanity.com/paper/2009.12414/) (2020)

## Abstract (arXiv)

Natural Language Query (NLQ) allows users to search and interact with information systems using plain, human language instead of structured query syntax. This paper presents a technical blueprint on the design of a modern NLQ system tailored to financial knowledge search. The introduction of NLQ not only enhances the precision and recall of the knowledge search compared to traditional methods, but also facilitates deeper insights by efficiently linking disparate financial objects, events, and relationships. Using core constructs from natural language processing, search engineering, and vector data models, the proposed system aims to address key challenges in discovering, relevance ranking, data freshness, and entity recognition intrinsic to financial data retrieval. In this work, we detail the unique requirements of NLQ for financial datasets and documents, outline the architectural components for offline indexing and online retrieval, and discuss the real-world use cases of enhanced knowledge search in financial services. We delve into the theoretical underpinnings and experimental evidence supporting our proposed architecture, ultimately providing a comprehensive analysis on the subject matter. We also provide a detailed elaboration of our experimental methodology, the data used, the results and future optimizations in this study.