Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision: Techniques and Use Cases

246 Pages·2023·40.725 MB·

by  L. Ashok Kumar, D. Karthika Renuka

Deep Learning Approach
for Natural Language
Processing, Speech,
and Computer Vision
Deep Learning Approach for Natural Language Processing, Speech, and Computer
Vision provides an overview of general deep learning methodology and its
applications of natural language processing (NLP), speech, and computer vision
tasks. It simplifies and presents the concepts of deep learning in a comprehensive
manner, with suitable, full-fledged examples of deep learning models, with an aim to
bridge the gap between the theoretical and the applications using case studies with
code, experiments, and supporting analysis.
Features:
• Covers latest developments in deep learning techniques as applied to audio
analysis, computer vision, and natural language processing.
• Introduces contemporary applications of deep learning techniques as applied
to audio, textual, and visual processing.
• Discovers deep learning frameworks and libraries for NLP, speech, and
computer vision in Python.
• Gives insights into using the tools and libraries in Python for real-world
applications.
• Provides easily accessible tutorials and real-world case studies with code to
provide hands-on experience.
This book is aimed at researchers and graduate students in computer engineering,
image, speech, and text processing.

Deep Learning Approach
for Natural Language
Processing, Speech, and
Computer Vision
Techniques and Use Cases
L. Ashok Kumar and D. Karthika Renuka
First edition published 2023
by CRC Press
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and by CRC Press
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CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 L. Ashok Kumar and D. Karthika Renuka
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ISBN: 978-1-032-39165-6 (hbk)
ISBN: 978-1-032-39166-3 (pbk)
ISBN: 978-1-003-34868-9 (ebk)
DOI: 10.1201/9781003348689
Typeset in Times
by Apex CoVantage, LLC
Dedication
To my wife Ms. Y. Uma Maheswari and daughter
A. K. Sangamithra, for their constant support and love.
—Dr. L. Ashok Kumar
To my parents Mr. N. Dhanaraj and Ms. D. Anuradha, who laid the foundation for
all my success, to my husband Mr. R. Sathish Kumar and my daughter P.S. Preethi
for their unconditional love and support for competition of this book, and to my
friend, brother and co-author of this book Dr. L. Ashok Kumar, who has been highly
credible and my greatest source of motivation and as a pillar of inspiration.
—Dr. D. Karthika Renuka

Contents
About the Authors ....................................................................................................xv
Preface ....................................................................................................................xvii
Acknowledgments ...................................................................................................xix
Chapter 1 Introduction ..........................................................................................1
Learning Outcomes ..............................................................................1
1.1 Introduction ...............................................................................1
1.1.1 Subsets of Artificial Intelligence .................................3
1.1.2 Three Horizons of Deep Learning Applications .........4
1.1.3 Natural Language Processing ......................................5
1.1.4 Speech Recognition .....................................................7
1.1.5 Computer Vision..........................................................7
1.2 Machine Learning Methods for NLP, Computer Vision
(CV), and Speech .....................................................................10
1.2.1 Support Vector Machine (SVM)................................10
1.2.2 Bagging .....................................................................12
1.2.3 Gradient-boosted Decision Trees (GBDTs) ..............13
1.2.4 Naïve Bayes ...............................................................13
1.2.5 Logistic Regression ...................................................15
1.2.6 Dimensionality Reduction Techniques ......................17
1.3 Tools, Libraries, Datasets, and Resources for the
Practitioners .............................................................................17
1.3.1 TensorFlow ................................................................20
1.3.2 Keras..........................................................................21
1.3.3 Deeplearning4j ..........................................................21
1.3.4 Caffe ..........................................................................21
1.3.5 ONNX .......................................................................21
1.3.6 PyTorch .....................................................................21
1.3.7 scikit-learn .................................................................22
1.3.8 NumPy.......................................................................22
1.3.9 Pandas .......................................................................22
1.3.10 NLTK ........................................................................23
1.3.11 Gensim ......................................................................23
1.3.12 Datasets .....................................................................24
1.4 Summary .....................................................................................26
Bibliography ................................................................................................26
Chapter 2 Natural Language Processing .............................................................27
Learning Outcomes ............................................................................27
2.1 Natural Language Processing ..................................................27
vii
viii Contents
2.2 Generic NLP Pipeline ..............................................................28
2.2.1 Data Acquisition ........................................................29
2.2.2 Text Cleaning ............................................................30
2.3 Text Pre-processing .................................................................31
2.3.1 Noise Removal ..........................................................31
2.3.2 Stemming ..................................................................31
2.3.3 Tokenization ..............................................................32
2.3.4 Lemmatization...........................................................33
2.3.5 Stop Word Removal ..................................................33
2.3.6 Parts of Speech Tagging ............................................34
2.4 Feature Engineering .................................................................34
2.5 Modeling ..................................................................................35
2.5.1 Start with Simple Heuristics.....................................35
2.5.2 Building Your Model .................................................35
2.5.3 Metrics to Build Model .............................................36
2.6 Evaluation ................................................................................36
2.7 Deployment .............................................................................38
2.8 Monitoring and Model Updating .............................................39
2.9 Vector Representation for NLP ...............................................39
2.9.1 One Hot Vector Encoding..........................................39
2.9.2 Word Embeddings .....................................................39
2.9.3 Bag of Words .............................................................41
2.9.4 TF-IDF ......................................................................41
2.9.5 N-gram ......................................................................42
2.9.6 Word2Vec ..................................................................42
2.9.7 Glove .........................................................................43
2.9.8 ElMo ..........................................................................43
2.10 Language Modeling with n-grams ..........................................44
2.10.1 Evaluating Language Models ....................................45
2.10.2 Smoothing .................................................................45
2.10.3 Kneser-Ney Smoothing .............................................46
2.11 Vector Semantics and Embeddings ...........................................46
2.11.1 Lexical Semantics .....................................................46
2.11.2 Vector Semantics .......................................................46
2.11.3 Cosine for Measuring Similarity ...............................47
2.11.4 Bias and Embeddings ................................................47
2.12 Summary ...................................................................................48
Bibliography ................................................................................................48
Chapter 3 State-of-the-Art Natural Language Processing ..................................49
Learning Outcomes ............................................................................49
3.1 Introduction .............................................................................49
3.2 Sequence-to-Sequence Models ................................................50
3.2.1 Sequence ...................................................................50
3.2.2 Sequence Labeling ....................................................50
3.2.3 Sequence Modeling ...................................................51
Contents ix
3.3 Recurrent Neural Networks .....................................................53
3.3.1 Unrolling RNN ..........................................................54
3.3.2 RNN-based POS Tagging Use Case ..........................56
3.3.3 Challenges in RNN ...................................................56
3.4 Attention Mechanisms .............................................................56
3.4.1 Self-attention Mechanism .........................................58
3.4.2 Multi-head Attention Mechanism .............................59
3.4.3 Bahdanau Attention ...................................................60
3.4.4 Luong Attention ........................................................61
3.4.5 Global Attention versus Local Attention ...................62
3.4.6 Hierarchical Attention ...............................................64
3.5 Transformer Model ..................................................................64
3.5.1 Bidirectional Encoder, Representations,
and Transformers (BERT) .........................................65
3.5.2 GPT3 .........................................................................72
3.6 Summary ..................................................................................74
Bibliography ................................................................................................74
Chapter 4 Applications of Natural Language Processing ...................................77
Learning Outcomes ............................................................................77
4.1 Introduction .............................................................................77
4.2 Word Sense Disambiguation ...................................................78
4.2.1 Word Senses ..............................................................78
4.2.2 WordNet: A Database of Lexical Relations ..............79
4.2.3 Approaches to Word Sense Disambiguation .............79
4.2.4 Applications of Word Sense Disambiguation ...........81
4.3 Text Classification ...................................................................82
4.3.1 Building the Text Classification Model .....................82
4.3.2 Applications of Text Classification ...........................83
4.3.3 Other Applications ....................................................84
4.4 Sentiment Analysis ..................................................................84
4.4.1 Types of Sentiment Analysis .....................................84
4.5 Spam Email Classification .......................................................86
4.5.1 History of Spam ........................................................87
4.5.2 Spamming Techniques ..............................................87
4.5.3 Types of Spams .........................................................87
4.6 Question Answering ................................................................88
4.6.1 Components of Question Answering System ............88
4.6.2 Information Retrieval-based Factoid Question
and Answering ...........................................................90
4.6.3 Entity Linking ...........................................................91
4.6.4 Knowledge-based Question Answering ....................92
4.7 Chatbots and Dialog Systems ..................................................93
4.7.1 Properties of Human Conversation ...........................93
4.7.2 Chatbots.....................................................................94
4.7.3 The Dialog-state Architecture ...................................96