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Python Natural Language Processing Cookbook

You're reading from Python Natural Language Processing Cookbook Over 50 recipes to understand, analyze, and generate text for implementing language processing tasks

Product type Paperback

Published in Mar 2021

Publisher Packt

ISBN-13 9781838987312

Length 284 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Zhenya Antić

View More author details

Table of Contents (10) Chapters

Preface

1. Chapter 1: Learning NLP Basics

2. Chapter 2: Playing with Grammar FREE CHAPTER

3. Chapter 3: Representing Text – Capturing Semantics

4. Chapter 4: Classifying Texts

5. Chapter 5: Getting Started with Information Extraction

6. Chapter 6: Topic Modeling

7. Chapter 7: Building Chatbots

8. Chapter 8: Visualizing Text Data

9. Other Books You May Enjoy

Training your own embeddings model

We can now train our own word2vec model on a corpus. For this task, we will use the top 20 Project Guttenberg books, which includes The Adventures of Sherlock Holmes. The reason for this is that training a model on just one book will result in suboptimal results. Once we get more text, the results will be better.

Getting ready

You can download the dataset for this recipe from Kaggle: https://www.kaggle.com/currie32/project-gutenbergs-top-20-books. The dataset includes files in RTF format, so you will have to save them as text. We will use the same package, gensim, to train our custom model.

We will use the pickle package to save the model on disk. If you do not have it installed, install it by using pip:

pip install pickle

How to do it…

We will read in all 20 books and use the text to create a word2vec model. Make sure all the books are located in one directory. Let's get started:

Import the necessary packages...

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Authors (1)

Zhenya Antić

Zhenya Antić

Zhenya Antić, Ph.D. is an expert in AI and NLP. She is currently the Director of AI Automation at Arch Insurance, where she leads initiatives in Intelligent Document Processing and applies various AI solutions to complex problems. With extensive consulting experience, Zhenya has worked on numerous NLP projects with various companies. She holds a Ph.D. in Linguistics from the University of California, Berkeley, and a B.S. in Computer Science from the Massachusetts Institute of Technology.

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