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

Chapter 2 – Text Representation

  1. The advantage of Bag-of-N-words over BoW is its ability to capture meaningful phrases that exist in two or more words.
  2. TF-IDF takes into account the importance of words within a document and across a collection of documents. It considers both the frequency of a term within a document (TF) and how unique or rare that term is across all documents (IDF). This results in TF-IDF giving higher weights to words that are more discriminative and informative, while downweighting common words.
  3. Although BoW and TF-IDF may appear simple, they already have real-world applications. One important application is to prevent spam emails from going to the inbox folder of an email account. BoW or TF-IDF helps you to distinguish the characteristics of a spam email from regular emails.