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)

What is Automated Text Summarization?

Automated text summarization is the process of using natural language processing (NLP) tools to produce concise versions of text that preserve all the key information present in the original content.

Content providers have adapted to our change in reading habits and it's now quite conventional to see shorter articles and posts. The other key adaptation has been providing summaries and time estimates for content. These features are developed via NLP and there has been continuous progress in the development of techniques and tools for text summarization.

Some of these tools are frameworks, such as Gensim and NLTK, that contain algorithms for text summarization. They also have easy-to-use interfaces. These have become quite useful for machine learning engineers and data scientists, who are continuously looking at solving business problems without having to know too much about the research that happened behind the scenes. We will be looking...