Natural Language Processing

Natural Language Processing

by Derek Rivera

ISBN9781806248988
PublisherDigital Drive Learning
Copyright Year2026
Price$257.00
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Description

Natural Language Processing (NLP) is a field within artificial intelligence that focuses on enabling machines to understand, interpret, and generate human language. NLP bridges the gap between human communication and computer understanding, allowing machines to process and analyze large amounts of natural language data, such as text and speech. Its applications are widespread in industries like healthcare, finance, customer service, and entertainment, impacting everything from chatbots to translation services. At its core, NLP involves several key tasks, including tokenization, part-of-speech tagging, named entity recognition (NER), and syntactic parsing. Tokenization breaks text into smaller units, such as words or sentences, while part-of-speech tagging labels each word with its grammatical role. Named entity recognition identifies entities like people, organizations, and locations, and syntactic parsing analyzes the grammatical structure of sentences. Another important aspect of NLP is semantic analysis, which focuses on understanding the meaning of words and sentences in context. Word embeddings, such as Word2Vec and GloVe, represent words as vectors in a high-dimensional space, capturing semantic relationships between words. Deep learning models like recurrent neural networks (RNNs) and transformers have greatly advanced NLP by enabling machines to handle more complex language tasks, such as sentiment analysis, machine translation, and text summarization. Overall, NLP continues to evolve, pushing the boundaries of how machines interact with human language. Natural Language Processing provides a comprehensive guide to the principles, techniques, and applications of NLP, enabling machines to understand and interact with human language. Contents: 1. Introduction, 2. Machine-based Neural Systems, 3. Sequential Neural Networks, 4. Automata Theory and Regular Languages, 5. Methods: Rules, Statistics, Neural Networks, 6. Neural Models of Speech Production, 7. Typical Tasks in Natural Language Processing, 8. Machine Language, 9. Exploring Evolutionary and Neurolinguistic Sciences.

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