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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Our data


First, we will discuss the data we will use for text generation and various preprocessing steps employed to clean data.

About the dataset

First, we will understand what the dataset looks like so that when we see the generated text, we can assess whether it makes sense, given the training data. We will download the first 100 books from the website https://www.cs.cmu.edu/~spok/grimmtmp/. These are translations of a set of books (from German to English) by the Brothers Grimm. This is the same as the text used in Chapter 6, Recurrent Neural Networks, for demonstrating the performance of RNNs.

Initially, we will download the first 100 books from the website with an automated script, as follows:

url = 'https://www.cs.cmu.edu/~spok/grimmtmp/'

# Create a directory if needed
dir_name = 'stories'
if not os.path.exists(dir_name):
    os.mkdir(dir_name)
    
def maybe_download(filename):
  """Download a file if not present"""
  print('Downloading file: ', dir_name+ os.sep+filename)
    
  if not...
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