Get to grips with real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualization, with this practical guide
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 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.
This 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.
