Applied Natural Language Processing
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Applied Natural Language Processing in the Enterprise
Author: Ankur A. Patel
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-05-12
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
Applied Natural Language Processing with AllenNLP
"Applied Natural Language Processing with AllenNLP" "Applied Natural Language Processing with AllenNLP" is a comprehensive guide for practitioners and researchers aiming to harness the full capabilities of modern deep learning in Natural Language Processing (NLP). Beginning with a survey of the field’s rapid evolution—from symbolic paradigms to state-of-the-art neural architectures—the book introduces readers to core tasks such as classification, sequence labeling, and question answering, illustrating their real-world applicability and production challenges. Designed for both newcomers and seasoned professionals, the opening chapters set the stage for why AllenNLP stands out among modern NLP frameworks, emphasizing its modular design, extensibility, and robust research ecosystem. The book meticulously unpacks the architecture and workflow of AllenNLP, delving into its building blocks: dataset readers, vocabularies, embedders, encoders, and model orchestration. Readers are guided through the intricacies of data preprocessing, experiment configuration via JSONNet, and the construction of custom components for advanced experimentation. With dedicated chapters on embedding techniques, model architecture, and efficient training practices, the book empowers readers to implement sophisticated models using state-of-the-art contextual representations, transformer architectures, and multitask learning strategies—all while emphasizing reproducibility, scalability, and robust evaluation. Transitioning from theory to practice, the text presents in-depth case studies on essential NLP tasks including sequence labeling, classification, semantic parsing, and coreference resolution. Subsequent chapters highlight critical pillars such as model explainability, fairness, and deployment best practices, including scalable REST API serving, container orchestration, and pipeline automation. The concluding sections navigate advanced integration, extension with third-party libraries, and the trajectory of NLP’s future—positioning AllenNLP as a vital tool from pioneering research to industrial deployment, across interdisciplinary domains.
Applied Natural Language Processing with Python
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.