- eBook:Python Natural Language Processing Cookbook: Over 50 recipes to understand, analyze, and generate different texts to implement language processing tasks
- Author:Zhenya Antic
- Data:April 9, 2021
- Pages:238 pages
- Format:PDF, ePUB
- A recipe-based guide to analyzing varying complexities of text using the natural language toolkit (NLTK)
- Implement common and not-so-common linguistic processing tasks using Python libraries
- Overcome the common challenges faced while implementing NLP pipelines
Book DescriptionPython 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 techniques for working with text from the 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, text classification, and parts of speech tagging to help you to structure your data. You'll then learn dependency parsing, discover different ways of representing text using BERT, and understand the basic implementation of a semantic search for text classification. As you make progress, 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, to be able to use these later for your projects. Additionally, the book covers developing chatbots, keyword matching, and visualizing text data.
By the end of this NLP book, you'll be able to work with a powerful set of tools for processing text and extracting different types of data from it, such as sentiment, names, topics, and much more.
What you will learn
- Become well-versed with basic and advanced NLP techniques in Python
- Find out how to pull structured data from large amounts of unstructured text
- Explore different techniques for topic modeling such as K-means, LDA, NMF, and BERT
- Work with visualization techniques such as NER, topic modeling, and word clouds for different NLP tools
- Build a basic chatbot with keyword matching, clustering, and deep learning
- Extract information from text using regular expression techniques and neural network tools
Who This Book Is ForThis book is for NLP practitioners, data scientists, and professionals working with text as part of their projects. Knowledge of Python and the basics of NLP will help you to make the most out of this book.
2. Playing with Grammar
3. Representing Text – Capturing Semantics
4. Classifying Texts
5. Getting Started with Information Extraction
6. Topic Modeling
7. Building Chatbots
8. Visualizing Text Data