Book Image

Python Natural Language Processing Cookbook

By : Zhenya Antić
Book Image

Python Natural Language Processing Cookbook

By: Zhenya Antić

Overview of this book

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.
Table of Contents (10 chapters)

Putting documents into a bag of words

A bag of words is the simplest way of representing text. We treat our text as a collection of documents, where documents are anything from sentences to book chapters to whole books. Since we usually compare different documents to each other or use them in a larger context of other documents, typically, we work with a collection of documents, not just a single document.

The bag of words method uses a training text that provides it with a list of words that it should consider. When encoding new sentences, it counts the number of occurrences each word makes in the document, and the final vector includes those counts for each word in the vocabulary. This representation can then be fed into a machine learning algorithm.

The decision of what represents a document lies with the engineer, and in many cases will be obvious. For example, if you are working on classifying tweets as belonging to a particular topic, a single tweet will be your document...