Book Image

The Handbook of NLP with Gensim

By : Chris Kuo
Book Image

The Handbook of NLP with Gensim

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)
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

Chapter 7 – Using Word2Vec

  1. The distributional hypothesis says that words that occur in similar contexts tend to have similar meanings. For example, the words cat and dog, temple and monk, or king and queen are sometimes seen together. In contrast, the words iron and monk, or car and sky appear less often in the same contexts.
  2. The first advantage of Word2Vec is that it can capture semantic relationships between words. Words with similar meanings or related concepts tend to have vectors that are close together. The second advantage is its ability to present contextual information. By considering the surrounding words, it captures the meaning of a word based on its usage and co-occurrence patterns. This allows Word2Vec to capture nuances in word meaning and understand different senses of a word based on its context. The third advantage is dimensionality reduction. Word2Vec presents the high-dimensional space of words in lower-dimensional word vectors. The fourth advantage...