Summary of Integrating Natural Language Processing Techniques Of Text Mining Into Financial System: Applications and Limitations, by Denisa Millo et al.
Integrating Natural Language Processing Techniques of Text Mining Into Financial System: Applications and Limitations
by Denisa Millo, Blerina Vika, Nevila Baci
First submitted to arxiv on: 29 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel research study explores the utilization of natural language processing (NLP) techniques in various financial system components, such as asset pricing, corporate finance, derivatives, risk management, and public finance. The study reviews literature from 2018 to 2023, highlighting the need for addressing specific problems. The authors identify the most commonly used text mining approaches combining probabilistic with vector-space models, processing information using classification techniques, and employing algorithms like long-short term memory (LSTM) and bidirectional encoder models. The research focuses on asset pricing, proposing an engineering perspective path for analyzing financial text. However, challenges persist in data quality, context-adaption, and model interpretability, hindering the integration of advanced NLP models in enhancing financial analysis and prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how technology, like natural language processing (NLP), can help with finance. It reviews what people have written about this topic over the past five years. The researchers find that most studies combine different approaches to analyze text data. They also notice that many algorithms are used to process information. The main area of focus is asset pricing, and they suggest ways for other researchers to study financial text. But there are still some big challenges, like making sure the data is good, adapting to different contexts, and understanding how models work. |
Keywords
» Artificial intelligence » Classification » Encoder » Lstm » Natural language processing » Nlp » Vector space
