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Transformative natural language processingbridging ambiguity in healthcare, legal, and financial applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Transformative natural language processingedited by Akshi Kumar, Saurabh Raj Sangwan.
Reminder of title:	bridging ambiguity in healthcare, legal, and financial applications /
other author:	Kumar, Akshi.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xii, 239 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science)
Online resource:	https://doi.org/10.1007/978-3-031-88988-2
ISBN:	9783031889882$q(electronic bk.)

Transformative natural language processingbridging ambiguity in healthcare, legal, and financial applications /
Transformative natural language processingbridging ambiguity in healthcare, legal, and financial applications /[electronic resource] :edited by Akshi Kumar, Saurabh Raj Sangwan. - Cham :Springer Nature Switzerland :2025. - xii, 239 p. :ill., digital ;24 cm.

Preface -- 1. Introduction to Natural Language Processing in High-Stakes Domains -- 2. NLP in Medicine: Enhancing Diagnostics and Patient Care -- 3. NLP in the Legal Domain: Ensuring Precision and Compliance -- 4. Introduction to NLP in Finance: Sentiment Analysis and Risk Management -- 5. Managing Uncertainty in NLP: Advanced Techniques and Approaches -- 6. NLP for Fraud Detection and Security in Financial Documents -- 7. Multilingual and Cross-Linguistic Challenges in NLP -- 8. NLP in Action: Case Studies from Healthcare, Finance, and Industry -- 9. Generative Large Language Models in Clinical, Legal and Financial Domains -- 10. Responsible and Ethical AI in Natural Language Processing.

The evolving landscape of technology has presented numerous opportunities for addressing some of the most critical challenges in high-stakes domains such as medicine, law, and finance. These fields, where the stakes are exceptionally high, have increasingly turned to Natural Language Processing (NLP) to manage, interpret, and utilize vast amounts of unstructured linguistic data. The complexities and subtleties inherent in human language pose significant challenges in these sectors, where precision and clarity are paramount. Misinterpretation or ambiguity can lead to far-reaching consequences, making the need for advanced NLP techniques crucial. This book aims to bridge the gap between state-of-the-art NLP technologies and their practical applications in medicine, law, and finance. By focusing on the specific challenges and advancements within these sectors, the publication intends to highlight innovative approaches, methodologies, and technologies that are shaping the future of NLP. It discusses the integration of NLP with other technological advancements, the development of new tools and techniques, and the ethical considerations involved in deploying NLP solutions in high-stakes domains. Moreover, the book provides a platform for researchers, practitioners, and industry experts to share their experiences, insights, and research findings. Through comprehensive reviews, case studies, and empirical research, it covers a range of topics including but not limited to handling uncertainty in clinical notes, approaches for dealing with ambiguity in legal documents, sentiment analysis in financial markets, and ethical considerations in the use of NLP for sensitive data.

ISBN: 9783031889882$q(electronic bk.)

Standard No.: 10.1007/978-3-031-88988-2doiSubjects--Topical Terms:

200539
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Transformative natural language processingbridging ambiguity in healthcare, legal, and financial applications /
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505 0 $a Preface -- 1. Introduction to Natural Language Processing in High-Stakes Domains -- 2. NLP in Medicine: Enhancing Diagnostics and Patient Care -- 3. NLP in the Legal Domain: Ensuring Precision and Compliance -- 4. Introduction to NLP in Finance: Sentiment Analysis and Risk Management -- 5. Managing Uncertainty in NLP: Advanced Techniques and Approaches -- 6. NLP for Fraud Detection and Security in Financial Documents -- 7. Multilingual and Cross-Linguistic Challenges in NLP -- 8. NLP in Action: Case Studies from Healthcare, Finance, and Industry -- 9. Generative Large Language Models in Clinical, Legal and Financial Domains -- 10. Responsible and Ethical AI in Natural Language Processing.
520 $a The evolving landscape of technology has presented numerous opportunities for addressing some of the most critical challenges in high-stakes domains such as medicine, law, and finance. These fields, where the stakes are exceptionally high, have increasingly turned to Natural Language Processing (NLP) to manage, interpret, and utilize vast amounts of unstructured linguistic data. The complexities and subtleties inherent in human language pose significant challenges in these sectors, where precision and clarity are paramount. Misinterpretation or ambiguity can lead to far-reaching consequences, making the need for advanced NLP techniques crucial. This book aims to bridge the gap between state-of-the-art NLP technologies and their practical applications in medicine, law, and finance. By focusing on the specific challenges and advancements within these sectors, the publication intends to highlight innovative approaches, methodologies, and technologies that are shaping the future of NLP. It discusses the integration of NLP with other technological advancements, the development of new tools and techniques, and the ethical considerations involved in deploying NLP solutions in high-stakes domains. Moreover, the book provides a platform for researchers, practitioners, and industry experts to share their experiences, insights, and research findings. Through comprehensive reviews, case studies, and empirical research, it covers a range of topics including but not limited to handling uncertainty in clinical notes, approaches for dealing with ambiguity in legal documents, sentiment analysis in financial markets, and ethical considerations in the use of NLP for sensitive data.
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