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Palash Goyal - Deep Learning for Natural Language Processing: Creating Neural Networks with Python

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Palash Goyal Deep Learning for Natural Language Processing: Creating Neural Networks with Python

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Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models.

Youll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system.

This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways.

What You Will Learn

  • Gain the fundamentals of deep learning and its mathematical prerequisites

  • Discover deep learning frameworks in Python

  • Develop a chatbot

  • Implement a research paper on sentiment classification

Who This Book Is For

Software developers who are curious to try out deep learning with NLP.

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Palash Goyal - Deep Learning for Natural Language Processing: Creating Neural Networks with Python
Deep Learning for Natural Language Processing: Creating Neural Networks with Python
Palash Goyal
Deep Learning for Natural Language Processing: Creating Neural Networks with Python

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Palash Goyal, Sumit Pandey, Karan Jain 2018
Palash Goyal , Sumit Pandey and Karan Jain Deep Learning for Natural Language Processing
1. Introduction to Natural Language Processing and Deep Learning
Palash Goyal 1, Sumit Pandey 1 and Karan Jain 1
(1)
Bangalore, Karnataka, India
Natural language processing (NPL) is an extremely difficult task in computer science. Languages present a wide variety of problems that vary from language to language. Structuring or extracting meaningful information from free text represents a great solution, if done in the right manner. Previously, computer scientists broke a language into its grammatical forms, such as parts of speech, phrases, etc., using complex algorithms. Today, deep learning is a key to performing the same exercises.
This first chapter of Deep Learning for Natural Language Processing offers readers the basics of the Python language, NLP, and Deep Learning. First, we cover the beginner-level codes in the Pandas, NumPy, and SciPy libraries. We assume that the user has the initial Python environment (2.x or 3.x) already set up, with these libraries installed. We will also briefly discuss commonly used libraries in NLP, with some basic examples. Finally, we will discuss the concepts behind deep learning and some common frameworks, such as TensorFlow and Keras. Then, in later chapters, we will move on to providing a higher level overview of NLP.
Depending on the machine and version preferences, one can install Python by using the following references:
  • www.python.org/downloads/
  • www.continuum.io/downloads
The preceding links and the basic packages installations will provide the user with the environment required for deep learning.
We will be using the following packages to begin. Please refer to the following links, in addition to the package name for your reference :
Python Machine Learning
  • Pandas ( http://pandas.pydata.org/pandas-docs/stable )
  • NumPy ( www.numpy.org )
  • SciPy ( www.scipy.org )
Python Deep Learning
  • TensorFlow ( http://tensorflow.org/ )
  • Keras ( https://keras.io/ )
Python Natural Language Processing
  • Spacy ( https://spacy.io/ )
  • NLTK ( www.nltk.org/ )
  • TextBlob ( http://textblob.readthedocs.io/en/dev/ )
We might install other related packages, if required, as we proceed. If you are encountering problems at any stage of the installation, please refer to the following link: https://packaging.python.org/tutorials/installing-packages/ .
Note
Refer to the Python package index, PyPI ( https://pypi.python.org/pypi ), to search for the latest packages available.
Follow the steps to install pip via https://pip.pypa.io/en/stable/installing/ .
Python Packages
We will be covering the references to the installation steps and the initial-level coding for the Pandas, NumPy, and SciPy packages. Currently, Python offers versions 2.x and 3.x, with compatible functions for machine learning. We will be making use of Python2.7 and Python3.5, where required. Version 3.5 has been used extensively throughout the chapters of this book.
NumPy
NumPy is used particularly for scientific computing in Python. It is designed to efficiently manipulate large multidimensional arrays of arbitrary records, without sacrificing too much speed for small multidimensional arrays. It could also be used as a multidimensional container for generic data. The ability of NumPy to create arrays of arbitrary type, which also makes NumPy suitable for interfacing with general-purpose data-base applications, makes it one of the most useful libraries you are going to use throughout this book, or thereafter for that matter.
Following are the codes using the NumPy package. Most of the lines of code have been appended with a comment , to make them easier to understand by the user.
## Numpy
import numpy as np # Importing the Numpy package
a= np.array([1,4,5,8], float) # Creating Numpy array with Float variables
print(type(a)) #Type of variable
>
# Operations on the array
a[0] = 5 #Replacing the first element of the array
print(a)
> [ 5. 4. 5. 8.]
b = np.array([[1,2,3],[4,5,6]], float) # Creating a 2-D numpy array
b[0,1] # Fetching second element of 1st array
> 2.0
print(b.shape) #Returns tuple with the shape of array
> (2, 3)
b.dtype #Returns the type of the value stored
> dtype('float64')
print(len(b)) #Returns length of the first axis
> 2
2 in b #'in' searches for the element in the array
> True
0 in b
> False
# Use of 'reshape' : transforms elements from 1-D to 2-D here
c = np.array(range(12), float)
print(c)
print(c.shape)
print('---')
c = c.reshape((2,6)) # reshape the array in the new form
print(c)
print(c.shape)
> [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.]
(12,)
---
[[ 0. 1. 2. 3. 4. 5.] [ 6. 7. 8. 9. 10. 11.]]
(2, 6)
c.fill(0) #Fills whole array with single value, done inplace
print(c)
> [[ 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0.]]
c.transpose() #creates transpose of the array, not done inplace
> array([[ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.]])
c.flatten() #flattens the whole array, not done inplace
> array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
# Concatenation of 2 or more arrays
m = np.array([1,2], float)
n = np.array([3,4,5,6], float)
p = np.concatenate((m,n))
print(p)
> [ 1. 2. 3. 4. 5. 6.]
(6,)
print(p.shape)
# 'newaxis' : to increase the dimensonality of the array
q = np.array([1,2,3], float)
q[:, np.newaxis].shape
> (3, 1)
NumPy has other functions, such as zeros , ones , zeros_like , ones_like , identity , eye , which are used to create arrays filled with 0s, 1s, or 0s and 1s for given dimensions.
Addition, subtraction, and multiplication occur on same-size arrays. Multiplication in NumPy is offered as element-wise and not as matrix multiplication. If the arrays do not match in size, the smaller one is repeated to perform the desired operation. Following is an example for this:
a1 = np.array([[1,2],[3,4],[5,6]], float)
a2 = np.array([-1,3], float)
print(a1+a2)
> [[ 0. 5.] [ 2. 7.] [ 4. 9.]]
Note
pi and e are included as constants in the NumPy package.
One can refer to the following sources for detailed tutorials on NumPy: www.numpy.org/ and https://docs.scipy.org/doc/numpy-dev/user/quickstart.html .
NumPy offers few of the functions that are directly applicable on the arrays: sum (summation of elements), prod (product of the elements), mean (mean of the elements), var (variance of the elements), std (standard deviation of the elements), argmin (index of the smallest element in array), argmax (index of the largest element in array), sort (sort the elements), unique (unique elements of the array).
a3 = np.array([[0,2],[3,-1],[3,5]], float)
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