دانلود کتاب پردازش زبان طبیعی – کتاب درسی با پیاده‌سازی پایتون

Natural language processing (NLP) and its associated applications have flourished due to advancements in artificial intelligence (AI) over the past few decades. NLP applications include information retrieval (IR) systems, text summarization (TS) systems, question-and-answering (chatbot) systems, as well as recent developments in large language models (LLMs) and generative AI (GenAI). These topics are prevalent in both industry and academia, offering varied routines that significantly enhance a wide range of everyday services. The objective of this book is to provide readers with foundational NLP concepts and knowledge through 14 h of step-by-step workshops. These workshops will guide participants in practicing various core Python-based NLP tools, including NLTK, spaCy, TensorFlow, Keras, transformer, and BERT technology, to build their own Python-based NLP applications. This book is structured into two main parts: Part I—Concepts and Technology, and Part II—Natural Language Processing Workshops with Python Implementation in 14 Hours. In Part I, the first ten chapters lay a solid foundation in natural language processing (NLP) concepts, exploring key topics such as N-gram language models, part-of-speech tagging, syntax analysis, semantic representations, and latest advancements in transfer learning and transformer technology. Each chapter builds on the preceding one, leading to an understanding of major NLP applications and the evolution of large language models and generative AI, while also addressing ethical considerations in AI. Part II consists of seven practical workshops that provide hands-on experience with Python implementations relevant to the concepts discussed in the first part. Starting with an introduction to the Natural Language Toolkit (NLTK), the workshops guide readers through various NLP tasks such as N-gram modeling, sentiment analysis, and use of transformers like BERT. Each workshop offers step-by-step instructions, empowering readers to apply their knowledge in real-world scenarios, including the creation and deployment of a chatbot system. This structured approach balances foundational theory with practical application, making it suitable for learners eager to advance their skills in NLP.