Transformative Natural Language Processing : Bridging Ambiguity in Healthcare, Legal, and Financial Applications - Akshi Kumar

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Transformative Natural Language Processing

Bridging Ambiguity in Healthcare, Legal, and Financial Applications

By: Akshi Kumar (Editor), Saurabh Raj Sangwan (Editor)

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Hardcover | 21 July 2025

At a Glance

Hardcover
252 Pages

Dimensions(cm)
23.5 x 15.5

Hardcover

$299.00

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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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