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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

From LDA to Ensemble LDA

Suppose a corpus has three distinct words, and the three words belong to three topics. This idea is shown in Figure 13.1, in which the vertices of the simplex are the three words. The three topics are labeled as Topic A, Topic B, and Topic C in the left simplex. However, LDA may identify a fourth topic from the combination of the three topics. It is a “pseudo” topic, as shown in the middle of the simplex.

Figure 13.1 – Applying the ensembling method to LDA

Figure 13.1 – Applying the ensembling method to LDA

Let’s take an ensembling approach by building many LDA models on the same data. Most of the LDA models will have topics A, B, and C, and some other LDA models will produce pseudo topics in addition to topics A, B, and C. The “true” topics A, B, and C shall appear more frequently and the “pseudo” topics shall appear less frequently. This idea is demonstrated in the simplex on the right-hand side of Figure 14.1. All the blue...

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