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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

Text Wrangling and Preprocessing

Almost 80% of NLP is data preprocessing. When we do topic modeling using TF-IDF, LDA, LSA, or similar models, we need to prepare the texts. Without text preprocessing, the quality of the model outcome will suffer, and latent information may be buried in the ocean of texts. The well-known phrase garbage in, garbage out (GIGO) refers to this. In this chapter, we will learn the key steps in NLP preprocessing: tokenization, lowercase conversion, stop word removal, punctuation removal, stemming, and lemmatization. The first two of these are very basic, so we will spend more time on the rest. We will learn how to code these steps in spaCy, NLTK, and Gensim. Later, we will build a pipeline for NLP preprocessing applicable to any NLP preprocessing in the future.

Specifically, we will cover the following topics:

  • Steps in NLP preprocessing
  • Coding with spaCy
  • Coding with NLTK
  • Coding with Gensim
  • Building a pipeline with spaCy

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