Название: Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python, Second Edition
Автор: Akshay Kulkarni, Adarsha Shivananda
Издательство: Apress
Год: 2021
Страниц: 302
Язык: английский
Формат: pdf (true), epub
Размер: 11.6 MB

Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP.

Natural Language Processing Recipes is a handy problem/solution reference for learning and implementing NLP solutions using Python. The book is packed with lots of code and approaches that help you quickly learn and implement both basic and advanced NLP techniques. You will learn how to efficiently use a wide range of NLP packages, implement text classification, and identify parts of speech. You also learn about topic modeling, text summarization, text generation, sentiment analysis, and many other NLP applications. This new edition of Natural Language Processing Recipes focuses on implementing end-to-end projects using Python and leveraging cutting-edge algorithms and transfer learning.

The book begins by discussing text data collections, web scraping, and different types of data sources. You learn how to clean and preprocess text data and analyze it using advanced algorithms. Throughout the book, you explore the semantic as well as syntactic analysis of text. It covers complex NLP solutions that involve text normalization, various advanced preprocessing methods, part-of-speech (POS) tagging, parsing, text summarization, sentiment analysis, topic modeling, named-entity recognition (NER), word2vec, seq2seq, and more.

The book covers both fundamental and state-of-the-art techniques used in machine learning applications and deep learning natural language processing. This edition includes various advanced techniques to convert text to features, like GloVe, ELMo, and BERT. It also explains how transformers work, using Sentence-BERT and GPT as examples. In this second edition, few advanced state-of-art embeddings and industrial applications are explained along with end-to-end implementation using deep learning.

Who This Book Is For:
This book explains various concepts and implementations to get more clarity when applying NLP algorithms to chosen data. You learn about all the ingredients you need to become successful in the NLP space. Fundamental Python skills are assumed, as well as some knowledge of machine learning and basic NLP. If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master natural language processing, this learning path will do you a lot of good. All you need to know are the basics of machine learning and Python to enjoy the book.