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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

adaptive radius 217

artificial intelligence (AI) 4

B

Bag-of-N-grams 20

coding 26

using, with NLTK 29, 30

using, with scikit-learn 28, 29

with Gensim 26-28

Bag-of-Visual-Words (BoVW) 21

Bag-of-Words (BoW) 19, 61, 233

coding 22

real-world applications 21

with Gensim 22-24

with scikit-learn 24-26

used, for performing word embedding 79-81

Bayes’ theorem 176, 178

Bernoulli distribution 149

binary outcomes 150

defining 149

example 149

overview 149

BERT-based word embeddings 231

Bidirectional Encoder Representations from Transformers (BERT) 190, 227, 230, 231

BERTopic 227

BERT 231

building 233

c-TFIDF 233

data loading 234

document information, obtaining 237, 239

HDBSCAN 232

keywords of single topic...

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