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)

High-Level View of Text Summarization

We can look at the topic of text summarization from different angles. The following figure shows how we will proceed:

Figure 6.1: This diagram shows the ways in which we can classify text summarization

We will discuss each aspect of text suammarization in detail in the coming sections.

Purpose

There are a number of different angles from which we can view the topic of text summarization. First, we can look at the purpose of text summarization, where we are interested in knowing what people generally use it for. This is further divided into three parts:

  • Generic summarization: Text summarizers can operate on any text input of sufficient length and they can perform adequately on sources from many different domains. If your project objectives do not care what the domain is and can accept sources from many different fields, then you can use generic summarizers, possibly provided in a tool such as Gensim.
  • Domain...