Natural Language Processing with Python

di Dr. Bharti Salunke (Autore)

Dr. Rajesh Doriya (Autore)

Dr. Sharad Salunke (Autore)

Xoffencer International Book Publication, 2025

Natural Language Processing (NLP) is a rapidly evolving field within artificial intelligence that focuses on the interaction between computers and human languages. It is concerned with the ability of machines to read, understand, and generate human language in a way that is both meaningful and contextually relevant. The integration of NLP with Python has revolutionized this domain, as Python's simplicity, versatility, and extensive libraries make it an ideal tool for developing NLP applications. This abstract delves into the essential aspects of NLP using Python, exploring key concepts, tools, and techniques that enable machines to process and analyze large amounts of natural language data. At its core, NLP involves several fundamental tasks, including tokenization, part-of-speech tagging, named entity recognition, syntactic parsing, and sentiment analysis. Python, with its rich ecosystem of libraries such as NLTK, spaCy, and transformers, provides an accessible and robust framework for tackling these tasks. Tokenization, for instance, breaks down text into smaller units such as words or sentences, which forms the foundation for many NLP applications. Part-of-speech tagging assigns grammatical labels to words, while named entity recognition identifies specific entities like names, dates, or locations within the text. Syntactic parsing helps in understanding the grammatical structure of sentences, and sentiment analysis enables machines to determine the emotional tone of a piece of text. One of the significant advancements in NLP is the application of machine learning techniques to language processing. Python's libraries such as scikit-learn, TensorFlow, and PyTorch offer powerful tools for training models that can predict and classify language data. Deep learning models, particularly those based on neural networks, have led to major breakthroughs in tasks like machine translation, speech recognition, and question answering. Pre-trained models like BERT and GPT, implemented through Python frameworks, have set new benchmarks in NLP, allowing developers to build more sophisticated and accurate systems with minimal training data.

• Autore: Dr. Bharti Salunke, Dr. Rajesh Doriya, Dr. Sharad Salunke
• Anno edizione: 2025
• Editore: Xoffencer International Book Publication
• Formato: EPUB2 con DRMFREE
• Lingua: Inglese
• Compatibilità: Tutti i dispositivi (eccetto Kindle)
• Layout: Reflowable
• EAN: 9789348116321