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)

Using word embeddings

In this recipe we switch gears and learn how to represent words using word embeddings, which are powerful because they are a result of training a neural network that predicts a word from all other words in the sentence. The resulting vector embeddings are similar for words that occur in similar contexts. We will use the embeddings to show these similarities.

Getting ready

In this recipe, we will use a pretrained word2vec model, which can be found at http://vectors.nlpl.eu/repository/20/40.zip. Download the model and unzip it in the Chapter03 directory. You should now have a file whose path is …/Chapter03/40/model.bin.

We will also be using the gensim package to load and use the model. Install it using pip:

pip install gensim

How to do it…

We will load the model, demonstrate some features of the gensim package, and then compute a sentence vector using the word embeddings. Let's get started:

  1. Import the KeyedVectors object...