From Text to Ticker: A Comprehensive Survey and Methodological Guide to Named Entity Recognition in Finance
- Title
- From Text to Ticker: A Comprehensive Survey and Methodological Guide to Named Entity Recognition in Finance
- Creator
- Vidhani, Nilesh; Ramasamy, Gobi; Syam Mohan, E.
- Description
- The financial industry generates vast volumes of unstructured textual data from sources such as regulatory filings, news articles, social media, and earnings call transcripts. Extracting structured and actionable intelligence from this data remains a significant challenge. Named Entity Recognition (NER) is a fundamental task in natural language processing that supports this process by identifying and categorizing key information within text. However, the linguistic complexity, contextual ambiguity, and domain-specific terminology of financial text require specialized approaches that extend beyond general-purpose NER models. This paper presents a comprehensive survey and methodological guide to Financial Named Entity Recognition (Fin-NER). It begins by introducing the core concepts of NER while highlighting the unique challenges posed by financial text. The paper then reviews the evolution of Fin-NER approaches, ranging from rule-based systems and classical machine learning techniques to modern deep learning architectures. Furthermore, it analyzes the distinction between fine-tuned transformer-based models and general-purpose large language models in the current research. The study also examines commonly used datasets and evaluation metrics for benchmarking Fin-NER systems. Finally, it discusses key findings, existing methodological limitations, and future research directions, including hybrid modeling strategies, cross-lingual datasets, and the development of more reliable and explainable systems. Overall, this work serves both as a scholarly review of the Fin-NER field and as a practical guide for researchers and practitioners seeking to transform unstructured financial text into structured and informative representations. 2026 IEEE.
- Source
- Proceedings of 6th International Conference on Expert Clouds and Applications, ICOECA 2026;pp.749-755
- Date
- 01-01-2026
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Benchmark Analysis; Deep Learning; Domain Adaptation; Financial Natural Language Processing (Fin-NLP); Financial Text Mining; FinBERT; Information Extraction; Large Language Models (LLMs); Named Entity Recognition (NER); Transformer Models
- Coverage
- Vidhani N., Christ University, Department of Computer Science, Bangalore, India; Ramasamy G., Christ University, Department of Computer Science, Bangalore, India; Syam Mohan E., Christ University, Department of Computer Science, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833157451-2;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Vidhani, Nilesh; Ramasamy, Gobi; Syam Mohan, E., “From Text to Ticker: A Comprehensive Survey and Methodological Guide to Named Entity Recognition in Finance,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26069.