Book Image

The Natural Language Processing Workshop

By : Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning
5 (1)
Book Image

The Natural Language Processing Workshop

5 (1)
By: Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning

Overview of this book

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.
Table of Contents (10 chapters)
Preface

Topic-Modeling Algorithms

Topic-modeling algorithms operate on the following assumptions:

  • Topics contain a set of words.
  • Documents are made up of a set of topics.

Topics can be considered to be a weighted collection of words. After these common assumptions, different algorithms diverge in how they go about discovering topics. In the upcoming sections, we will cover in detail three topic-modeling algorithms—namely LSA, LDA, and HDP. Here, the term latent (the L in these acronyms) refers to the fact that the probability distribution of the topics is not directly observable. We can observe the documents and the words but not the topics.

Note

The LDA algorithm builds on the LSA algorithm. In this case, similar acronyms are indicative of this association.

Latent Semantic Analysis (LSA)

We will start by looking at LSA. LSA actually predates the World Wide Web. It was first described in 1988. LSA is also known by an alternative name, Latent Semantic Indexing...