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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

Word2Vec for 10-K financial documents to the SEC

Financial documents have a large amount of unstructured data too. This use case has been chosen in this book because it applies NLP techniques to financial documents. Professionals in the financial services industry may find this use case helpful.

Background

A 10-K financial document is an annual report filled in by a publicly traded company on its financial performance. It is required by the US Securities and Exchange Commission (SEC). While a 10-K report has many numbers and tables, there are textual sections on Risk Factors (Item 1A), Management’s Discussion and Analysis (Item 7), and Quantitative and Qualitative Disclosures about Market Risks (Item 7A). These textual sections represent the perspective of management about the business of the company.

Questions

How do we apply NLP techniques to financial documents?

NLP solution

The author of [8] applied Word2Vec to get the word embeddings. The author then performed...

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