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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

Comparing Word2Vec with Doc2Vec, GloVe, and fastText

There are a few similar techniques to Word2Vec, including Doc2Vec, GloVec, and fastText. As we are close to the end of the chapter, let’s spend some time discussing them and comparing the differences.

Word2Vec versus Doc2Vec

Both Word2Vec and Doc2Vec are based on the distributional hypothesis. While Word2Vec focuses on learning vector representations for individual words, Doc2Vec extends Word2Vec to learn vector representations for entire documents or paragraphs. In terms of the modeling approach (https://arxiv.org/abs/1310.4546), Word2Vec takes a sequence of words as input and learns word embeddings. Doc2Vec takes a sequence of words along with an additional document ID (or label) as input. It learns document embeddings by predicting words in the context of the document ID. The document ID acts as an additional input signal, helping the model to differentiate between different documents. We will learn about Doc2Vec...

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