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Key benefits

Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensim

Implement common and not-so-common linguistic processing tasks using Python libraries

Overcome the common challenges faced while implementing NLP pipelines

Description

Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization. Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You’ll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you’ll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data. By the end of this NLP book, you’ll have developed the skills to use a powerful set of tools for text processing.

What you will learn

Become well-versed with basic and advanced NLP techniques in Python

Represent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddings

Perform text classification using different methods, including SVMs and LSTMs

Explore different techniques for topic modeling such as K-means, LDA, NMF, and BERT

Work with visualization techniques such as NER and word clouds for different NLP tools

Build a basic chatbot using NLTK and Rasa

Extract information from text using regular expression techniques and statistical and deep learning tools

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Transformers for Natural Language Processing

€67.99

Python Natural Language Processing Cookbook

€41.99

Advanced Natural Language Processing with TensorFlow 2

€32.99

Total € 142.97

IG May 25, 2022

Among other programming "cookbooks" I thought this one to be the best. In contrast to other NLP books, this one reads easily and has clear examples.I learned lots of new things, e.g., I discovered NMF topic modeling for the first time.Chapters about Rasa and SVM turned out to be very useful. I was never interested in chatbots, but decided to write one (a simple ToDo system), and the book helped to figure it out. Also, the graphs are cool, I never knew it was possible to visualize text this way.For those whose code doesn't run because they loaded package versions that don't match the book, the book github repository will load the package version that matches the book.

Amazon Verified review

Alsida D. Jul 26, 2021

Really a nice read for whoever is using Python in their daily work and started with NLP text processing analyses. The book explains details and code examples from the NLP basics such as parsing parts of speech to advanced topics like information extraction and developing chatbots using state-of-art platforms/libraries from NLTK and Rasa . The source code of all the examples is available in github and I suggest that you follow them as you are reading. A typical learn by doing book I would say :)

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Steve-3PO Dec 15, 2021

Zhenya Antić's Python NLP Cookbook is a great resource for jumpstarting any NLP project. I worked as a graduate TA for introductory and advanced NLP courses at my university. I would recommend this book to anyone working on an NLP project, from beginning students taking an introductory NLP course to the seasoned computational linguist.

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Josip Sep 23, 2021

The book is a practical introduction to tasks commonly used in NLP, covering multiple topics, ranging from classical NLP methods like tokenization, POS tagging, stemming, lemmatization and stop word removal, to syntactic analysis including dependency parsing, entity and relation extraction and anaphora resolution, over semantics from simple bag-of-words representations over neural word embeddings and state-of-the-art, transformer-based text document representation, to applications like text clustering and topic modelling, text classification, information extraction using topic models, to building chatbots and visualization of text data.The main advantage of this book is the hands-on approach. Each of the chapters has the same structure ("Getting ready", "How to do it...", "How it works...", "There's more...", "See also"). "How to do it..." sections are accompanied by the Python code that demonstrates the concept introduced in the chapter, while the "How it works..." section explains the idea behind the code. Each part of code is explained, which is very handy for learn-by-doing oriented students.The book is primarily aimed as an intro to the applied NLP, so researchers or practitioners with more experience won't get a lot out of it. Each task description is quite short, so it would go well with a book that covers a more holistic approach that describes in more depth why a specific task is performed and what is its role in a bigger NLP system.

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Dmitry Jul 26, 2021

Recently, I went through ML and Deep Learning courses that have left me with a feeling that I understand how typical NLP models work from the inside, but without a feeling that I can just go and implement a basic chat bot or semantic search for a website.This is where this cookbook was so helpful. The book gives simple recipes of a basic chatbot, an embeddings model, named entity recognition model and many more. Each recipe comes with an explanation of how it works and lets you get to a simple working solution quickly(most of the recipes took me 10 to 30 minutes). After a simple solution is ready you can continue experimentation on your own.The book uses nltk, keras, tensorflow, pytorch, transformers, huggingface, rasa, spacy, and other modern and mature nlp frameworks. Although in the future I can imagine how some of these will become less relevant, right now these frameworks are very popular and I see them used in production solutions.

Amazon Verified review

Python Natural Language Processing Cookbook: Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

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