Natural Language Processing using R Pocket Primer

Master NLP with R using practical techniques and tools, featuring in-depth coverage of modern NLP models and libraries.Key FeaturesComprehensive introduction to NLP concepts and techniques using RPractical examples using popular NLP librariesDetailed coverage of advanced models like BERT and GPTBook DescriptionThis book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to a technical audience, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life. The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field. Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.What you will learnInstall and configure R and RStudioWork with vectors, matrices, and data frames in RImplement text normalization and tokenizationApply word embeddings and vectorization techniquesUtilize popular NLP libraries in RDevelop advanced models like BERT and GPTWho this book is forIdeal for developers with a basic understanding of R and machine learning, this book is designed for those looking to enhance their skills in NLP, providing a comprehensive introduction to fundamental concepts, tools, and applications. Prior experience with R and basic ML concepts is recommended.