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Teaching Computers to Read : Effective Best Practices in Building Valuable NLP Solutions - Rachel Wagner-Kaiser

Teaching Computers to Read

Effective Best Practices in Building Valuable NLP Solutions

By: Rachel Wagner-Kaiser

eText | 5 November 2025 | Edition Number 1

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Building Natural Language Processing (NLP) solutions that deliver ongoing business value is not straightforward. This book provides clarity and guidance on how to design, develop, deploy, and maintain NLP solutions that address real-world business problems.

In this book, we discuss the main challenges and pitfalls encountered when building NLP solutions. We also outline how technical choices interact with (and are impacted by) data, tools, the business goals, and integration between human experts and the artificial intelligence (AI) solution. The best practices we cover here do not depend on cutting-edge modeling algorithms or the architectural flavor of the month. We provide practical advice for NLP solutions that are adaptable to the solution's specific technical building blocks.

Through providing best practices across the lifecycle of NLP development, this handbook will help organizations - particularly technical teams - use critical thinking to understand how, when, and why to build NLP solutions, what the common challenges are, and how to address or avoid those challenges. These best practices help organizations deliver consistent value to their stakeholders and deliver on the promise of AI and NLP.

A code companion for the book is available here: https://github.com/TeachingComputersToRead/TC2R-CodeCompanion

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You Can Find This eBook In

Non-FictionEconomicsEconometricsEconomic StatisticsComputing & I.T.Computer ScienceArtificial IntelligenceMachine LearningDigital Lifestyle & Online World: Consumer & User GuidesComputer Games & Online Games Strategy GuidesDatabasesData Mining
Natural Language & Machine TranslationMathematicsProbability & StatisticsComputer Programming & Software DevelopmentProgramming & Scripting Languages

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Hardcover

Published: 5th November 2025

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