Book Image

The Handbook of NLP with Gensim

By : Chris Kuo
Book Image

The Handbook of NLP with Gensim

By: Chris Kuo

Overview of this book

Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios. You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications. By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.
Table of Contents (24 chapters)
Part 1: NLP Basics
Part 2: Latent Semantic Analysis/Latent Semantic Indexing
Part 3: Word2Vec and Doc2Vec
Part 4: Topic Modeling with Latent Dirichlet Allocation
Part 5: Comparison and Applications

Part 1: NLP Basics

In this part, you will get an overview of NLP. You will understand the concept of text representation and learn two basic forms of word embeddings. You will learn the key steps in NLP preprocessing including tokenization, lowercase conversion, stop words removal, punctuation removal, stemming, and lemmatization. You will learn how to do coding with spaCy, NLTK, and Gensim, and know how to build a pipeline applicable for any NLP preprocessing in the future.

This part contains the following chapters: