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Full Description
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 DescriptionNavigating 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 forThis 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
Table of Contents
Introduction to NLP
Word Embedding
Text Wrangling and Preprocessing
Latent Semantic Analysis with scikit-learn
Cosine Similarity
Latent Semantic Indexing with Gensim
Using Word2Vec
Doc2Vec with Gensim
Understanding Discrete Distributions
Latent Dirichlet Allocation
LDA Modeling
LDA Visualization
The Ensemble LDA for Model Stability
LDA and BERTopic
Real-World Use Cases
