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)
1
Part 1: NLP Basics
5
Part 2: Latent Semantic Analysis/Latent Semantic Indexing
9
Part 3: Word2Vec and Doc2Vec
12
Part 4: Topic Modeling with Latent Dirichlet Allocation
18
Part 5: Comparison and Applications

Text Representation

A computer operates on zeros and ones, and algorithms operate on numerical values. A computer does not understand beautiful texts such as the plays by William Shakespeare or the books by Leo Tolstoy. So, raw texts need to be converted to numerical values for a computer to process. The first step in NLP is converting texts to numerical values.

In this chapter, we will learn about the basic text representation – Bag-of-Words, Bag-of-N-grams, and TF-IDF. This chapter is for absolute NLP beginners. In this chapter, we will learn how to code with Gensim, scikit-learn, and NLTK. We will cover the following topics:

  • What text representation is
  • The transition from one-hot encoding to Bag-of-Words to Bag-of-N-grams
  • What TF-IDF is
  • How to perform Bag-of-Words (BoW) and TF-IDF encoding in Gensim
  • The real-world applications of BoW and TF-IDF

By the end of this chapter, you will be able to describe the BoW, Bag-of-N-grams, and TF-IDF methods...