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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

Describing how BERTopic works

BERTopic uses the BERT word embedding vectors for topic modeling [5]. A document is first to be embedded into word vectors. The high-dimensional word vectors then go through dimensionality reduction in order to be clustered into topics. BERTopic has a sequence of five modular components, as shown in Figure 14.5. The five modules are designed to be as independent as possible so that data scientists can choose an alternative technique for a module. For instance, the clustering method HDSCAN can be replaced with K-means. These techniques are the default components for BERTopic. At the end of the chapter, I will illustrate how to model with their alternative techniques.

Figure 14.5 – BERTopic structure

Figure 14.5 – BERTopic structure

Let’s review each technique in the next sections.

BERT – word embeddings

The first block converts a document into numerical representations. BERTopic uses the BERT-based word embeddings of paraphrase...

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