Book Image

Natural Language Processing Fundamentals

By : Sohom Ghosh, Dwight Gunning
Book Image

Natural Language Processing Fundamentals

By: Sohom Ghosh, Dwight Gunning

Overview of this book

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this book, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.
Table of Contents (10 chapters)

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 are not observed but are assumed to be hidden generators of words. After these assumptions, different algorithms diverge in how they go about discovering topics. In this chapter, we will cover two topic modeling algorithms, namely LSA and LDA. Both models will be discussed in detail in the coming sections.

Latent Semantic Analysis

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 acronym, Latent Semantic Indexing (LSI), particularly when it is used for semantic searches of document indexes. The goal of LSA is to uncover the latent topics that underlie documents and words. The assumption is that these latent topics drive the distribution of words in the document. In the next section, we will learn about...