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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

Latent Semantic Analysis with scikit-learn

Searching for keywords has become part of our information lives. We use keyword searches to find articles. When we type in words, we do not mean to retrieve articles with the exact words but the concepts. For example, when we search for “Tesla” and “electric car,” we want to get articles about the company Tesla and its electric cars, rather than historical information about the scientist Nicola Tesla. This is called a “semantic search,” which means interpreting a word for its intent and contextual meaning. We use keywords as cues to get the concept and the documents that are associated with that concept, rather than using the keywords at their face values.

Latent Semantic Analysis (LSA) is a milestone solution that was developed in the 1990s. When words are entered for a keyword search, LSA finds the underlying topics that the words in documents associate with and retrieves those documents. LSA...

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