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)

Cleaning Text Data

Most of the time, text data cannot be used as it is. This is because the presence of various unknown symbols or links makes it dirty or unfit for use. Data cleaning is the art of extracting meaningful portions from data by eliminating unnecessary details. Consider the sentence, He tweeted, 'Live coverage of General Elections available at this.tv/show/ge2019. _/\_ Please tune in :) '.

Various symbols, such as "_/\_" and ":)," are present in the sentence. They do not contribute much to its meaning. We need to remove such unwanted details. This is done not only to focus more on the actual content but also to reduce computations. To achieve this, methods such as tokenization and stemming are used. We will learn about them one by one in the upcoming sections.

Tokenization

Tokenization and word tokenizers were briefly described in Chapter 1, Introduction to Natural Language Processing. Tokenization is the process of splitting sentences...