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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

Interpretable text classification from electronic health records

We can use many NLP techniques with EHRs to find their semantic relationships, as we saw in a previous use case. However, how do we know the resulting topics make sense? In this use case, we will see their proposal for objective evaluation metrics for the topic results.

Background

As we said when discussing previous use cases, the clinical notes in EHRs have great possibilities for predictive tasks. Various topic modeling techniques can be applied to texts. Using topic models allows us to use topics as features. Data science researchers come to realize that the interpretability of these classification models is the key aspect.

Questions

It is one thing to build many topic models, but selecting the most appropriate model for production use is not trivial. Is there an objective and systematic way to compare models? What are good evaluation metrics?

NLP solution

The authors [8] believe that interpretability...

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