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Hands-On Natural Language Processing with Python

You're reading from   Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781789139495
Length 312 pages
Edition 1st Edition
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Authors (5):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Chaitanya Joshi Chaitanya Joshi
Author Profile Icon Chaitanya Joshi
Chaitanya Joshi
Auguste Byiringiro Auguste Byiringiro
Author Profile Icon Auguste Byiringiro
Auguste Byiringiro
Rajesh Arumugam Rajesh Arumugam
Author Profile Icon Rajesh Arumugam
Rajesh Arumugam
Karthik Muthuswamy Karthik Muthuswamy
Author Profile Icon Karthik Muthuswamy
Karthik Muthuswamy
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Toc

Table of Contents (15) Chapters Close

Preface 1. Getting Started 2. Text Classification and POS Tagging Using NLTK FREE CHAPTER 3. Deep Learning and TensorFlow 4. Semantic Embedding Using Shallow Models 5. Text Classification Using LSTM 6. Searching and DeDuplicating Using CNNs 7. Named Entity Recognition Using Character LSTM 8. Text Generation and Summarization Using GRUs 9. Question-Answering and Chatbots Using Memory Networks 10. Machine Translation Using the Attention-Based Model 11. Speech Recognition Using DeepSpeech 12. Text-to-Speech Using Tacotron 13. Deploying Trained Models 14. Other Books You May Enjoy

Training the model

We will train a model for matching these question pairs. Let's start by importing the relevant libraries, as follows:

import sys
import os
import pandas as pd
import numpy as np
import string
import tensorflow as tf

Following is a function that takes a pandas series of text as input. Then, the series is converted to a list. Each item in the list is converted into a string, made lower case, and stripped of surrounding empty spaces. The entire list is converted into a NumPy array, to be passed back:

def read_x(x):
x = np.array([list(str(line).lower().strip()) for line in x.tolist()])
return x

Next up is a function that takes a pandas series as input, converts it to a list, and returns it as a NumPy array:

def read_y(y):
return np.asarray(y.tolist())

The next function splits the data for training and validation. Validation data is helpful to see how well...

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