Practices of Natural Language Processing in the Finance Sector

Bozyiğit, Fatma; Kılınç, Deniz

doi:10.1007/978-981-16-8997-0_9

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…DL models for NLP [78], such as transformer-based architectures are increasingly employed for analyzing economic texts, including policy documents, market reports, and social media discourse. These models excel in understanding and generating complex language structures, enabling sophisticated tasks such as sentiment analysis, topic extraction, and automated summarization, which are crucial for real-time economic decision-making and market trend analysis.…”

Section: Natural Language Processing Modelsmentioning

confidence: 99%

Deep learning for economic transformation: a parametric review

Tariq,

Ahmed,

Khan

et al. 2024

IJEECS

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Deep learning (DL) is increasingly recognized for its effectiveness in analyzing and forecasting complex economic systems, particularly in the context of Pakistan's evolving economy. This paper investigates DL's transformative role in managing and interpreting increasing volumes of intricate economic data, leading to more nuanced insights. DL models show a marked improvement in predictive accuracy and depth over traditional methods across various economic domains and policymaking scenarios. Applications include demand forecasting, risk evaluation, market trend analysis, and resource allocation optimization. These processes utilize extensive datasets and advanced algorithms to identify patterns that traditional methods cannot detect. Nonetheless, DL's broader application in economic research faces challenges like limited data availability, complexity of economic interactions, interpretability of model outputs, and significant computational power requirements. The paper outlines strategies to overcome these barriers, such as enhancing model interpretability, employing federated learning for better data privacy, and integrating behavioral and social economic theories. It concludes by stressing the importance of targeted research and ethical considerations in maximizing DL's impact on economic insights and innovation, particularly in Pakistan and globally.

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Section: Natural Language Processing Modelsmentioning

confidence: 99%

Deep learning for economic transformation: a parametric review

Tariq,

Ahmed,

Khan

et al. 2024

IJEECS

View full text Add to dashboard Cite

show abstract

“…In the extant literature, previous researchers have attempted to offer review papers on NLP from a generic perspective; however, those kinds of experiences cannot directly be duplicated in the government sector, which needs context-specific approaches to expand the knowledge to feasible digital implementations. In a wider context, there are context-specific literature reviews on NLP applications in health [14,15], marketing [16], and engineering [17], but little research focuses on NLP's adoption by governments and public administration. Earlier studies are mainly to provide a generic review in mapping analytical processes or applications of NLP in other domains, and a thorough understanding of NLP in government is still missing.…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review

Jiang,

Pang,

Wong

et al. 2023

Applied Sciences

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Natural language processing (NLP), which is known as an emerging technology creating considerable value in multiple areas, has recently shown its great potential in government operations and public administration applications. However, while the number of publications on NLP is increasing steadily, there is no comprehensive review for a holistic understanding of how NLP is being adopted by governments. In this regard, we present a systematic literature review on NLP applications in governments by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The review shows that the current literature comprises three levels of contribution: automation, extension, and transformation. The most-used NLP techniques reported in government-related research are sentiment analysis, machine learning, deep learning, classification, data extraction, data mining, topic modelling, opinion mining, chatbots, and question answering. Data classification, management, and decision-making are the most frequently reported reasons for using NLP. The salient research topics being discussed in the literature can be grouped into four categories: (1) governance and policy, (2) citizens and public opinion, (3) medical and healthcare, and (4) economy and environment. Future research directions should focus on (1) the potential of chatbots, (2) NLP applications in the post-pandemic era, and (3) empirical research for government work.

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Practices of Natural Language Processing in the Finance Sector

Cited by 2 publications

References 18 publications

Deep learning for economic transformation: a parametric review

Deep learning for economic transformation: a parametric review

Natural Language Processing Adoption in Governments and Future Research Directions: A Systematic Review

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