Natural Language Processing Fundamentals for Developers

Length: 364 pages
Edition: 1
Language: English
Publisher: Mercury Learning and Information
Publication Date: 2021-06-29
ISBN-10: 1683926579
ISBN-13: 9781683926573
Sales Rank: #0 (See Top 100 Books)

This book is for developers who are looking for an overview of basic concepts in Natural Language Processing. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The first chapter shows you various details of managing data that are relevant for NLP. The next pair of chapters contain NLP concepts, followed by another pair of chapters with Python code samples to illustrate those NLP concepts. Chapter 6 explores applications, e.g., sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and a short discussion regarding chatbots. The final chapter presents the Transformer architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years and considered SOTA (“state of the art”). The appendices contain introductory material (including Python code samples) on regular expressions and probability/statistical concepts. Companion files with source code and figures are included.

FEATURES:

Covers extensive topics related to natural language processing
Includes separate appendices on regular expressions and probability/statistics
Features companion files with source code and figures from the book.

Preface xiii
Chapter 1: Working with Data
What are Datasets?
Data Types
Preparing Datasets
Missing Data, Anomalies, and Outliers
What is Imbalanced Classification?
What is SMOTE?
Analyzing Classifiers (Optional)
The Bias-Variance Trade-Off
Summary
Chapter 2: NLP Concepts (I)
The Origin of Languages
The Complexity of Natural Languages
Japanese Grammar
Phonetic Languages
Multiple Ways to Pronounce Consonants
English Pronouns and Prepositions
What is NLP?
A Wide-Angle View of NLP
Information Extraction and Retrieval 59viii • Contents
Word Sense Disambiguation
NLP Techniques in ML
Text Normalization and Tokenization
Handling Stop Words
What is Stemming?
What is Lemmatization?
Working with Text: POS
Working with Text: NER
What is Topic Modeling?
Keyword Extraction, Sentiment Analysis, and
Text Summarization
Summary
Chapter 3: NLP Concepts (II)
What is Word Relevance?
What is Text Similarity?
Sentence Similarity
Working with Documents
Techniques for Text Similarity
What is Text Encoding?
Text Encoding Techniques
The BoW Algorithm
What are N-Grams?
Calculating tf, idf, and tf-idf
The Context of Words in a Document
What is Cosine Similarity?
Text Vectorization (A.K.A. Word Embeddings)
Overview of Word Embeddings and Algorithms
What is Word2vec?
The CBoW Architecture
What are Skip-grams?
What is GloVe?
Working with GloVe 111Contents • ix
What is FastText?
Comparison of Word Embeddings
What is Topic Modeling?
Language Models and NLP
Vector Space Models
NLP and Text Mining
Relation Extraction and Information Extraction
What is a BLEU Score?
Summary
Chapter 4: Algorithms and Toolkits (I)
What is NLTK?
NLTK and BoW
NLTK and Stemmers
NLTK and Lemmatization
NLTK and Stop Words
What Is Wordnet?
NLTK, lxml, and XPath
NLTK and N-Grams
NLTK and POS (I)
NLTK and POS (2)
NLTK and Tokenizers
NLTK and Context-Free Grammars (Optional)
What is Gensim?
An Example of Topic Modeling
A Brief Comparison of Popular Python-Based NLP Libraries
Miscellaneous Libraries
Summary
Chapter 5: Algorithms and Toolkits (II)
Cleaning Data with Regular Expressions
Handling Contracted Words
Python Code Samples of BoW
One-Hot Encoding Examples 174x • Contents
Sklearn and Word Embedding Examples
What is BeautifulSoup?
Web Scraping with Pure Regular Expressions
What is Scrapy?
What is SpaCy?
SpaCy and Stop Words
SpaCy and Tokenization
SpaCy and Lemmatization
SpaCy and NER
SpaCy Pipelines
SpaCy and Word Vectors
The ScispaCy Library (Optional)
Summary
Chapter 6: NLP Applications
What is Text Summarization?
Text Summarization with Gensim and SpaCy
What are Recommender Systems?
Content-Based Recommendation Systems
Collaborative Filtering Algorithm
Recommender Systems and Reinforcement
Learning (Optional)
What is Sentiment Analysis?
Sentiment Analysis with Naïve Bayes
Sentiment Analysis with VADER and NLTK
Sentiment Analysis with Textblob
Sentiment Analysis with Flair
Detecting Spam
Logistic Regression and Sentiment Analysis
Working with COVID-19
What are Chatbots?
Summary 246Contents • xi
Chapter 7: Transformer, BERT, and GPT
What is Attention?
An Overview of the Transformer Architecture
What is T5?
What is BERT?
The Inner Workings of BERT
Subword Tokenization
Sentence Similarity in BERT
Generating BERT Tokens (1)
Generating BERT Tokens (2)
The BERT Family
Introduction to GPT
Working with GPT-2
What is GPT-3?
The Switch Transformer: One Trillion Parameters
Looking Ahead
Summary
Appendix A: Introduction to Regular Expressions
Appendix B: Introduction to Probability and Statistics
Index