Natural Language Processing Fundamentals for Developers 1st Edition by Oswald Campesato ISBN 9781683926559 1683926552

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

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

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

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

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