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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

Variational E-M

Variational E-M is an extension of E-M that incorporates variational inference. In variational E-M, during the “expectation” step, instead of computing the exact posterior distribution of the latent variables as in standard E-M, it approximates this posterior using a simpler distribution from a predefined family. Then, during the “maximization” step, it optimizes the model parameters to maximize a lower bound on the likelihood of the observed data, which is derived from the approximate posterior. Variational E-M iterates between these two steps until convergence, providing a computationally efficient way to perform parameter estimation in complex probabilistic models, especially in Bayesian settings.

Now, let’s describe the variational E-M algorithm in our context:

  1. The E-step: We get the optimal values of the variational parameters, (γ, ϕ), in Eq. (11) and Eq. (12) for every document in the corpus by assuming...

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