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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

The process of Ensemble LDA

The Ensemble LDA algorithm is a combination of LDA and CBDBSCAN. Let’s break the process involved down into steps:

  1. Text preprocessing: Perform any necessary preprocessing steps such as tokenization, stop-word removal, and stemming on the documents.
  2. LDA training: Build multiple LDA models on the document collection using different random initializations.
  3. Topic assignment: For each document in the collection, assign a topic distribution based on the trained LDA models. This can be done by calculating the probability of each topic for the document using the LDA models.
  4. CBDBSCAN: Apply the CBDBSCAN algorithm to cluster the documents based on their assigned topics. CBDBSCAN is an extension of the DBSCAN algorithm that incorporates a checkback step to refine the clustering results. We will learn about DBSCAN and CBDBSCAN in the next section.
  5. Output: The output of the algorithm is a set of clusters, where each cluster represents a...

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