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The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

By : Chris Kuo
5 (6)
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The Handbook of NLP with Gensim

The Handbook of NLP with Gensim

5 (6)
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)
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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

Summary

In this chapter, we learned how LSI was developed based on SVD. We learned a large document-term matrix can be decomposed into three matrices through SVD. We also learned about a few basic properties of matrix operations and transformation matrices, as well as eigenvectors and eigenvalues, to understand SVD. After that, we applied SVD to real data to observe the outcome.

Gensim has packaged LSI in a few lines of code for efficient production. While this chapter walked you through the theoretical construction of LSI, Chapter 6, Latent Semantic Indexing with Gensim, will teach you how to build an LSI model for production. However, there is an important NLP concept that you should learn about before learning about LSI with Gensim. It is cosine similarity. It is a fundamental concept used extensively in the NLP field, including modern word embeddings and large language models. Let’s move on to the next chapter.

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