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)

Representing phrases – phrase2vec

Encoding words is useful, but usually, we deal with more complex units, such as phrases and sentences. Phrases are important because they specify more detail than just words. For example, the phrase delicious fried rice is very different than just the word rice.

In this recipe, we will train a word2vec model that uses phrases as well as words.

Getting ready

We will be using the Yelp restaurant review dataset in this recipe, which is available here: https://www.yelp.com/dataset (the file is about 4 GB.) Download the file and unzip it in the Chapter03 folder. I downloaded the dataset in September 2020, and the results in the recipe are from that download. Your results might differ, since the dataset is updated by Yelp periodically.

The dataset is multilingual, and we will be working with the English reviews. In order to filter them, we will need the langdetect package. Install it using pip:

pip install langdetect

How to do...