The Handbook of NLP with Gensim : Leverage Topic Modeling to Uncover Hidden Patterns, Themes, and Valuable Insights Within Textual Data / Chris Kuo.

Format:

Book

Author/Creator:

Kuo, Chris, author.

Language:

English

Subjects (All):

Natural language processing (Computer science)--Software.

Natural language processing (Computer science).

Python (Computer program language).

Open source software.

Physical Description:

1 online resource (310 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Summary:

Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projects Key Features Advance your NLP skills with this comprehensive guide covering detailed explanations and code practices Build real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal results Adhere to the real-world industrial applications of topic modeling in medical, legal, and other fields Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Convert text into numerical values such as bag-of-word, TF-IDF, and word embedding Use various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDA Build topical modeling pipelines and visualize the results of topic models Implement text summarization for legal, clinical, or other documents Apply core NLP techniques in healthcare, finance, and e-commerce Create efficient chatbots by harnessing Gensim's NLP capabilities Who this book is for This book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications.

Contents:

Cover

Title Page

Copyright and Credits

Contributors

Table of Contents

Preface

Part 1: NLP Basics

Chapter 1: Introduction to NLP

Introduction to natural language processing

NLU + NLG = NLP

NLU

NLG

Gensim and its NLP modeling techniques

BoW and TF-IDF

LSA/LSI

Word2Vec

Doc2Vec

LDA

Ensemble LDA

Topic modeling with BERTopic

Common NLP Python modules included in this book

spaCy

NLTK

Summary

Questions

References

Chapter 2: Text Representation

Technical requirements

What word embedding is

Simple encoding methods

One-hot encoding

BoW

Bag-of-N-grams

What TF-IDF is

Shining applications of BoW and TF-IDF

Coding - BoW

Gensim for BoW

scikit-learn for BoW (CountVectorizer)

Coding - Bag-of-N-grams

Gensim for N-grams

scikit-learn for N-grams

NLTK for N-grams

Coding - TF-IDF

Gensim for TF-IDF

scikit-learn for TF-IDF

Chapter 3: Text Wrangling and Preprocessing

Key steps in NLP preprocessing

Tokenization

Lowercase conversion

Stop word removal

Punctuation removal

Stemming

Lemmatization

Coding with spaCy

spaCy for lemmatization

spaCy for PoS

Coding with NLTK

NLTK for tokenization

NLTK for stop-word removal

NLTK for lemmatization

Coding with Gensim

Gensim for preprocessing

Gensim for stop-word removal

Gensim for stemming

Building a pipeline with spaCy

Part 2: Latent Semantic Analysis/Latent Semantic Indexing

Chapter 4: Latent Semantic Analysis with scikit-learn

Understanding matrix operations

An orthogonal matrix

The determinant of a matrix

Understanding a transformation matrix

A transformation matrix in daily life examples.

Understanding eigenvectors and eigenvalues

An introduction to SVD

Truncated SVD

Truncated SVD for LSI

Coding truncatedSVD with scikit-learn

Using TruncatedSVD

randomized_SVD

Using TruncatedSVD for LSI with real data

Loading the data

Creating TF-IDF

Using TruncatedSVD to build a model

Interpreting the outcome

Chapter 5: Cosine Similarity

What is cosine similarity?

How cosine similarity is used in images

How to compute cosine similarity with scikit-learn

Chapter 6: Latent Semantic Indexing with Gensim

Performing text preprocessing

Performing word embedding with BoW and TF-IDF

TF-IDF

Modeling with Gensim

Using the coherence score to find the optimal number of topics

Saving the model for production

Using the model as an information retrieval tool

Loading the dictionary list

Preprocessing the new document

Scoring the document to get the latent topic scores

Calculating the similarity scores with the new document

Finding documents with high similarity scores

Part 3: Word2Vec and Doc2Vec

Chapter 7: Using Word2Vec

Introduction to Word2Vec

Advantages of Word2Vec

Reviewing the real-world applications of Word2Vec

Introduction to Skip-Gram (SG)

Data preparation

The input and output layers

The hidden layer

Should I remove stop words for training Word2Vec?

Model computation

Introduction to CBOW

Using a pretrained model for semantic search

Adding and subtracting words/concepts

Example 1

Example 2

Visualizing Word2Vec with TensorBoard

Training your own Word2Vec model in CBOW and Skip-Gram

Load the data

Text preprocessing.

Training your own Word2Vec model in CBOW

Training your own Word2Vec model in Skip-Gram

Visualizing your Word2Vec model with t-SNE

Comparing Word2Vec with Doc2Vec, GloVe, and fastText

Word2Vec versus Doc2Vec

Word2Vec versus GloVe

Word2Vec versus FastText

Chapter 8: Doc2Vec with Gensim

From Word2Vec to Doc2Vec

PV-DBOW

The input layer

The output layer

Model optimization

PV-DM

The real-world applications of Doc2Vec

Doc2Vec modeling with Gensim

Text preprocessing for Doc2Vec

Modeling

Saving the model

Saving the training data

Putting the model into production

Loading the model

Loading the training data

Use case 1 - find similar articles

Use case 2 - find relevant documents based on keywords

Tips on building a good Doc2Vec model

Part 4: Topic Modeling with Latent Dirichlet Allocation

Chapter 9: Understanding Discrete Distributions

The basics of discrete probability distributions

Bernoulli distributions

The formal definition of a Bernoulli distribution

What does it look like?

Fun facts

Binomial distributions

The real-world examples

The formal definition of a binomial distribution

Plotting it with Python

Multinomial distributions

The formal definition of a multinomial distribution

Beta distributions

The formal definition of a beta distribution

The beta distribution in Bayesian inference

Fun fact

Dirichlet distributions

Real-world examples

The formal definition of a Dirichlet distribution

What is a simplex?.

What does the Dirichlet distribution look like?

The Dirichlet distribution in Bayesian inference

Chapter 10: Latent Dirichlet Allocation

What is generative modeling?

Discriminative modeling

Generative modeling

Bayes' theorem

Expectation-Maximization (EM)

Understanding the idea behind LDA

Dirichlet distribution of topics

Understanding the structure of LDA

Variational inference

Variational E-M

Gibbs sampling in LDA

Variational E-M versus Gibbs sampling

Chapter 11: LDA Modeling

Text preprocessing

Preprocessing

Experimenting with LDA modeling

A model built on BoW data

A model built on TF-IDF data

Building LDA models with a different number of topics

Models built on BoW data

Models built on TF-IDF data

Determining the optimal number of topics

Using the model to score new documents

Scoring new texts

Outcome

Chapter 12: LDA Visualization

Designing an infographic

Data visualization with pyLDAvis

The interactive graph

Chapter 13: The Ensemble LDA for Model Stability

From LDA to Ensemble LDA

The process of Ensemble LDA

Understanding DBSCAN and CBDBSCAN

DBSCAN

CBDBSCAN (Checkback DBSCAN)

Building an Ensemble LDA model with Gensim

Preprocessing the training data

Creating text representation with BOW and TF-IDF

Saving the dictionary

Building the Ensemble LDA model

Scoring new documents

Part 5: Comparison and Applications

Chapter 14: LDA and BERTopic

Understanding the Transformer model.

Understanding BERT

Describing how BERTopic works

BERT - word embeddings

UMAP - reduce the dimensionality of embeddings

HDBSCAN - cluster documents

c-TFIDF - create a topic representation

Maximal Marginal Relevance

Building a BERTopic model

Loading the data - no text preprocessing

Reviewing the results of BERTopic

Getting the topic information

Inspecting the keywords of a single topic

Getting document information

Getting representative documents

Visualizing the BERTopic model

Visualizing topics

Visualizing the hierarchy of topics

Visualizing the top words of topics

Visualizing on a heatmap

Predicting new documents

Using the modular property of BERTopic

Word embeddings

Dimensionality reduction

Clustering

Comparing BERTopic with LDA

Approach

Language understanding

Topic clarity

Determination of the number of topics

Determination of word significance in a topic

Chapter 15: Real-World Use Cases

Word2Vec for medical fraud detection

Background

NLP solution

Takeaways

Comparing LDA/NMF/BERTopic on Twitter/X posts

Interpretable text classification from electronic health records

BERTopic for legal documents

Word2Vec for 10-K financial documents to the SEC

Assessments

Chapter 1 - Introduction to NLP

Chapter 2 - Text Representation

Chapter 3 - Text Wrangling and Preprocessing.

Chapter 4 - Latent Semantic Analysis with scikit-learn.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781803245508

1803245506

OCLC:

1406408346