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10 books on Natural Language Processing [PDF]
Updated: March 07, 2024 | 24 |
Books on Natural Language Processing (NLP) serve as indispensable resources for startups dedicated to advancing the field of NLP and building innovative language-related technologies. These texts offer comprehensive insights into the foundational theories, techniques, and cutting-edge advancements in NLP, providing startups with the necessary knowledge to develop sophisticated language models, chatbots, sentiment analysis tools, language translation systems, and more.
1. Machine Learning and Deep Learning in Natural Language Processing
2023 by Anitha S. Pillai, Roberto Tedesco
"Exploring the intersection of Artificial Intelligence, linguistics, and computer science, 'Machine Learning and Deep Learning in Natural Language Processing' delves into the multifaceted realm of Natural Language Processing (NLP). NLP, a crucial sub-field of AI, focuses on the comprehension, generation, and recognition of both written and spoken human languages. This book offers a comprehensive overview of contemporary Neural Network techniques within the NLP domain, with a specific emphasis on applications such as Conversational Agents (chatbots), Text-to-Speech, and the nuanced handling of non-literal content, including emotions and satirical expressions. Notably, the text highlights the underexplored potential of NLP in healthcare, showcasing examples where it can make significant contributions, such as speech restoration, Parkinson's disease detection, and support for psychotherapists. Accessible to beginners seeking a foundational understanding of NLP techniques and beneficial for seasoned professionals looking to explore advanced topics like emotion management, empathy, and non-literal content, this book aims to bridge the knowledge gap and cater to a diverse audience."
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2. ChatGPT for Enterprise: Using Generative AI to bring AI to business
2023 by Jothi Periasamy
"ChatGPT for Enterprise: Using Generative AI to bring AI to business" is an illuminating guide that seamlessly integrates large language models (LLM) into corporate workflows, transforming the realm of Generative AI into a tangible reality. Drawing on in-depth analyses of case studies spanning diverse industries, such as retail, energy, and education, this book unravels the entire journey from conceptualization to practical implementation. Readers will gain profound insights into the intricacies of designing and constructing business applications empowered by ChatGPT and GPT. To expedite the deployment of LLM through GPT and ChatGPT modules, the book provides valuable GitHub links, as well as step-by-step procedures for module development, testing, fine-tuning, and deployment on the Google Cloud Platform (GCP). While catering to both business and technical audiences, the book primarily targets individuals interested in harnessing CGPT or ChatGPT models for Generative AI or LLM. Whether you're a seasoned professional or a newcomer to the world of Generative AI and LLM, this book serves as an excellent foundation for comprehending fundamental concepts and executing advanced use cases using the Google Cloud Platform.
Download PDF
3. Natural Language Processing: A Machine Learning Perspective
2021 by Yue Zhang, Zhiyang Teng
This comprehensive guide, "Natural Language Processing: A Machine Learning Perspective," takes a machine learning-centered approach with a reduced emphasis on linguistic intricacies. It offers a gentle introduction to natural language processing, building essential mathematical and deep learning models for NLP within a unified framework. NLP challenges are systematically categorized based on their machine learning characteristics, covering tasks such as classification, sequence labeling, and sequence-to-sequence problems. The book encompasses a wide range of topics, including statistical machine learning and deep learning models, text classification, and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based techniques. Throughout the text, it establishes strong connections between these concepts, empowering students to develop a profound grasp of NLP solutions, adapt existing models, and confidently forge innovative models of their own. With numerous examples, intuitive explanations, and end-of-chapter exercises, along with accessible sample code available online, this textbook proves to be an indispensable resource for upper undergraduate and graduate students.
Download PDF
4. Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques
2021 by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Explore the realm of natural language processing (NLP) through a series of hands-on projects that progressively increase in complexity, demonstrating advanced machine learning and deep learning techniques. This book guides you in using contemporary Python libraries and cutting-edge algorithms to develop comprehensive NLP solutions. Beginning with an overview of NLP and artificial intelligence to refresh your algorithmic knowledge, it then embarks on a journey through end-to-end NLP projects, starting with traditional algorithms and tackling tasks like sentiment analysis, emotion detection, topic modeling, and document clustering. The book extends its reach into e-commerce-focused projects, including product categorization, content retrieval, and content-based recommendation systems to elevate user experiences. As you delve deeper, you'll learn to create systems that identify similar sentences through contextual embedding, summarize extensive documents using recurrent neural networks (RNNs), offer automatic word suggestions using long short-term memory networks (LSTMs), and even construct a chatbot using transfer learning. The book concludes with a glimpse into the future of AI and cutting-edge research in the field.
Download PDF
5. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
In "Deep Learning for NLP and Speech Recognition," this textbook offers a detailed exploration of deep learning architecture and its practical applications across various Natural Language Processing (NLP) tasks, encompassing Document Classification, Machine Translation, Language Modeling, and Speech Recognition. Given the widespread integration of deep learning, NLP, and speech applications across diverse domains such as Finance, Healthcare, and Government, there arises an increasing demand for a comprehensive resource that effectively bridges deep learning techniques with NLP and speech, offering practical insights on leveraging these tools and libraries for real-world scenarios. This book not only elucidates contemporary deep learning methods relevant to NLP and speech but also presents cutting-edge approaches and incorporates hands-on experience through real-world case studies and accompanying code samples.
Download PDF
6. Deep Learning for Natural Language Processing: Creating Neural Networks with Python
2018 by Palash Goyal, Sumit Pandey, Karan Jain
Embark on a journey into the realm of deep learning tailored for natural language processing (NLP) through the pages of this book, replete with comprehensive demonstrations of neural network models like recurrent neural networks, long short-term memory networks, and sequence-to-sequence models. You'll commence by delving into the essential mathematical prerequisites and fundamental concepts underpinning both deep learning and NLP, all illustrated with hands-on examples. The initial chapters of this book lay the groundwork for NLP, commencing with word-vector representation and advancing to more sophisticated algorithms. The latter chapters exclusively focus on practical implementation, navigating the intricacies of advanced architectures such as RNN, LSTM, and Seq2seq, leveraging Python tools like TensorFlow and Keras. The book takes a progressive approach, culminating in the creation of a question-answer chatbot system that integrates the wealth of knowledge you've acquired throughout your journey.
Download PDF
7. Handbook of Natural Language Processing
2010 by Nitin Indurkhya, Fred J. Damerau
In its Second Edition, the Handbook of Natural Language Processing offers a comprehensive resource, equipping readers with practical methodologies and tools for the integration of natural language processing into computer systems. This revised edition not only eliminates obsolete content but also thoroughly refreshes each chapter while incorporating cutting-edge topics like sentiment analysis. This update introduces an even more extensive range of knowledge and insights for readers seeking to explore the evolving landscape of natural language processing.
Download PDF
8. Natural Language Processing with Python
2009 by Steven Bird, Ewan Klein, Edward Loper
This book provides a highly accessible introduction to the realm of natural language processing, a critical field supporting various language technologies, from predictive text and email filtering to automatic summarization and translation. Through this resource, you will acquire the skills needed to develop Python programs capable of handling extensive collections of unstructured text. You will gain proficiency in working with extensively annotated datasets using a comprehensive array of linguistic data structures and delve into the core algorithms for scrutinizing the content and structure of written communication. Bursting with illustrative examples and practical exercises, "Natural Language Processing with Python" equips you to extract insights from unstructured text, whether it's for topic inference or the identification of "named entities." Additionally, you will explore linguistic text analysis, encompassing parsing and semantic interpretation, and gain access to popular linguistic databases like WordNet and treebanks, all while integrating techniques from diverse fields such as linguistics and artificial intelligence.
Download PDF
9. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
2000 by Dan Jurafsky, James H. Martin
This book offers an empirical perspective on language processing by employing statistical and machine-learning algorithms in practical contexts, utilizing large-scale datasets as a foundation. Each chapter is constructed around detailed worked examples that serve to elucidate the central concepts and illuminate the relative merits and drawbacks of different approaches. Methodology boxes are thoughtfully integrated throughout, introducing essential tools like evaluation methods and the wizard of oz techniques. Each chapter also features problem sets for hands-on reinforcement of knowledge. Furthermore, the book breaks down the traditional boundaries between speech and text processing, merging these domains seamlessly. It comprehensively covers empiricist, statistical, and machine learning methodologies in language processing, encompassing both contemporary statistical approaches and earlier rule-based methods. The inclusion of modern and stringent evaluation metrics ensures readers are well-prepared for assessing language processing systems. This comprehensive resource unifies fundamental algorithms from various language processing domains, demonstrating their applicability across spoken and written language tasks, including speech recognition and word-sense disambiguation. Emphasizing the evolving landscape of the web and other platforms, the book provides a contemporary perspective on the field of language processing.
Download PDF
10. Foundations of Statistical Natural Language Processing
1999 by Christopher Manning, Hinrich Schutze
In the modern landscape of natural language text processing, statistical methodologies have risen to prominence. "Foundations of Statistical Natural Language Processing" emerges as the inaugural and all-encompassing guide to introduce readers to this statistical paradigm in natural language processing (NLP). Within the pages of this comprehensive volume, one will find a rich repository of theories and algorithms essential for crafting NLP tools. The book offers an extensive yet rigorous exploration of the mathematical and linguistic bedrock, accompanied by in-depth discussions on statistical techniques. This inclusive approach empowers both students and researchers to independently develop their NLP implementations. The book spans a diverse range of applications, including the identification of collocations, disambiguating word senses, employing probabilistic parsing, conducting information retrieval tasks, and tackling various other practical domains.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Machine Learning and Deep Learning in Natural Language Processing
2023 by Anitha S. Pillai, Roberto Tedesco
"Exploring the intersection of Artificial Intelligence, linguistics, and computer science, 'Machine Learning and Deep Learning in Natural Language Processing' delves into the multifaceted realm of Natural Language Processing (NLP). NLP, a crucial sub-field of AI, focuses on the comprehension, generation, and recognition of both written and spoken human languages. This book offers a comprehensive overview of contemporary Neural Network techniques within the NLP domain, with a specific emphasis on applications such as Conversational Agents (chatbots), Text-to-Speech, and the nuanced handling of non-literal content, including emotions and satirical expressions. Notably, the text highlights the underexplored potential of NLP in healthcare, showcasing examples where it can make significant contributions, such as speech restoration, Parkinson's disease detection, and support for psychotherapists. Accessible to beginners seeking a foundational understanding of NLP techniques and beneficial for seasoned professionals looking to explore advanced topics like emotion management, empathy, and non-literal content, this book aims to bridge the knowledge gap and cater to a diverse audience."
Download PDF
2. ChatGPT for Enterprise: Using Generative AI to bring AI to business
2023 by Jothi Periasamy
"ChatGPT for Enterprise: Using Generative AI to bring AI to business" is an illuminating guide that seamlessly integrates large language models (LLM) into corporate workflows, transforming the realm of Generative AI into a tangible reality. Drawing on in-depth analyses of case studies spanning diverse industries, such as retail, energy, and education, this book unravels the entire journey from conceptualization to practical implementation. Readers will gain profound insights into the intricacies of designing and constructing business applications empowered by ChatGPT and GPT. To expedite the deployment of LLM through GPT and ChatGPT modules, the book provides valuable GitHub links, as well as step-by-step procedures for module development, testing, fine-tuning, and deployment on the Google Cloud Platform (GCP). While catering to both business and technical audiences, the book primarily targets individuals interested in harnessing CGPT or ChatGPT models for Generative AI or LLM. Whether you're a seasoned professional or a newcomer to the world of Generative AI and LLM, this book serves as an excellent foundation for comprehending fundamental concepts and executing advanced use cases using the Google Cloud Platform.
Download PDF
3. Natural Language Processing: A Machine Learning Perspective
2021 by Yue Zhang, Zhiyang Teng
This comprehensive guide, "Natural Language Processing: A Machine Learning Perspective," takes a machine learning-centered approach with a reduced emphasis on linguistic intricacies. It offers a gentle introduction to natural language processing, building essential mathematical and deep learning models for NLP within a unified framework. NLP challenges are systematically categorized based on their machine learning characteristics, covering tasks such as classification, sequence labeling, and sequence-to-sequence problems. The book encompasses a wide range of topics, including statistical machine learning and deep learning models, text classification, and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based techniques. Throughout the text, it establishes strong connections between these concepts, empowering students to develop a profound grasp of NLP solutions, adapt existing models, and confidently forge innovative models of their own. With numerous examples, intuitive explanations, and end-of-chapter exercises, along with accessible sample code available online, this textbook proves to be an indispensable resource for upper undergraduate and graduate students.
Download PDF
4. Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques
2021 by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
Explore the realm of natural language processing (NLP) through a series of hands-on projects that progressively increase in complexity, demonstrating advanced machine learning and deep learning techniques. This book guides you in using contemporary Python libraries and cutting-edge algorithms to develop comprehensive NLP solutions. Beginning with an overview of NLP and artificial intelligence to refresh your algorithmic knowledge, it then embarks on a journey through end-to-end NLP projects, starting with traditional algorithms and tackling tasks like sentiment analysis, emotion detection, topic modeling, and document clustering. The book extends its reach into e-commerce-focused projects, including product categorization, content retrieval, and content-based recommendation systems to elevate user experiences. As you delve deeper, you'll learn to create systems that identify similar sentences through contextual embedding, summarize extensive documents using recurrent neural networks (RNNs), offer automatic word suggestions using long short-term memory networks (LSTMs), and even construct a chatbot using transfer learning. The book concludes with a glimpse into the future of AI and cutting-edge research in the field.
Download PDF
5. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
In "Deep Learning for NLP and Speech Recognition," this textbook offers a detailed exploration of deep learning architecture and its practical applications across various Natural Language Processing (NLP) tasks, encompassing Document Classification, Machine Translation, Language Modeling, and Speech Recognition. Given the widespread integration of deep learning, NLP, and speech applications across diverse domains such as Finance, Healthcare, and Government, there arises an increasing demand for a comprehensive resource that effectively bridges deep learning techniques with NLP and speech, offering practical insights on leveraging these tools and libraries for real-world scenarios. This book not only elucidates contemporary deep learning methods relevant to NLP and speech but also presents cutting-edge approaches and incorporates hands-on experience through real-world case studies and accompanying code samples.
Download PDF
6. Deep Learning for Natural Language Processing: Creating Neural Networks with Python
2018 by Palash Goyal, Sumit Pandey, Karan Jain
Embark on a journey into the realm of deep learning tailored for natural language processing (NLP) through the pages of this book, replete with comprehensive demonstrations of neural network models like recurrent neural networks, long short-term memory networks, and sequence-to-sequence models. You'll commence by delving into the essential mathematical prerequisites and fundamental concepts underpinning both deep learning and NLP, all illustrated with hands-on examples. The initial chapters of this book lay the groundwork for NLP, commencing with word-vector representation and advancing to more sophisticated algorithms. The latter chapters exclusively focus on practical implementation, navigating the intricacies of advanced architectures such as RNN, LSTM, and Seq2seq, leveraging Python tools like TensorFlow and Keras. The book takes a progressive approach, culminating in the creation of a question-answer chatbot system that integrates the wealth of knowledge you've acquired throughout your journey.
Download PDF
7. Handbook of Natural Language Processing
2010 by Nitin Indurkhya, Fred J. Damerau
In its Second Edition, the Handbook of Natural Language Processing offers a comprehensive resource, equipping readers with practical methodologies and tools for the integration of natural language processing into computer systems. This revised edition not only eliminates obsolete content but also thoroughly refreshes each chapter while incorporating cutting-edge topics like sentiment analysis. This update introduces an even more extensive range of knowledge and insights for readers seeking to explore the evolving landscape of natural language processing.
Download PDF
8. Natural Language Processing with Python
2009 by Steven Bird, Ewan Klein, Edward Loper
This book provides a highly accessible introduction to the realm of natural language processing, a critical field supporting various language technologies, from predictive text and email filtering to automatic summarization and translation. Through this resource, you will acquire the skills needed to develop Python programs capable of handling extensive collections of unstructured text. You will gain proficiency in working with extensively annotated datasets using a comprehensive array of linguistic data structures and delve into the core algorithms for scrutinizing the content and structure of written communication. Bursting with illustrative examples and practical exercises, "Natural Language Processing with Python" equips you to extract insights from unstructured text, whether it's for topic inference or the identification of "named entities." Additionally, you will explore linguistic text analysis, encompassing parsing and semantic interpretation, and gain access to popular linguistic databases like WordNet and treebanks, all while integrating techniques from diverse fields such as linguistics and artificial intelligence.
Download PDF
9. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
2000 by Dan Jurafsky, James H. Martin
This book offers an empirical perspective on language processing by employing statistical and machine-learning algorithms in practical contexts, utilizing large-scale datasets as a foundation. Each chapter is constructed around detailed worked examples that serve to elucidate the central concepts and illuminate the relative merits and drawbacks of different approaches. Methodology boxes are thoughtfully integrated throughout, introducing essential tools like evaluation methods and the wizard of oz techniques. Each chapter also features problem sets for hands-on reinforcement of knowledge. Furthermore, the book breaks down the traditional boundaries between speech and text processing, merging these domains seamlessly. It comprehensively covers empiricist, statistical, and machine learning methodologies in language processing, encompassing both contemporary statistical approaches and earlier rule-based methods. The inclusion of modern and stringent evaluation metrics ensures readers are well-prepared for assessing language processing systems. This comprehensive resource unifies fundamental algorithms from various language processing domains, demonstrating their applicability across spoken and written language tasks, including speech recognition and word-sense disambiguation. Emphasizing the evolving landscape of the web and other platforms, the book provides a contemporary perspective on the field of language processing.
Download PDF
10. Foundations of Statistical Natural Language Processing
1999 by Christopher Manning, Hinrich Schutze
In the modern landscape of natural language text processing, statistical methodologies have risen to prominence. "Foundations of Statistical Natural Language Processing" emerges as the inaugural and all-encompassing guide to introduce readers to this statistical paradigm in natural language processing (NLP). Within the pages of this comprehensive volume, one will find a rich repository of theories and algorithms essential for crafting NLP tools. The book offers an extensive yet rigorous exploration of the mathematical and linguistic bedrock, accompanied by in-depth discussions on statistical techniques. This inclusive approach empowers both students and researchers to independently develop their NLP implementations. The book spans a diverse range of applications, including the identification of collocations, disambiguating word senses, employing probabilistic parsing, conducting information retrieval tasks, and tackling various other practical domains.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
