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)

Introduction

In the previous chapter, we learned about the concepts of Natural Language Processing (NLP) and text analytics. We also looked at various pre-processing steps in brief. In this chapter, we will learn how to deal with text data whose formats are mostly unstructured. Unstructured data cannot be represented in a tabular format. Therefore, it is essential to convert it into numeric features because most machine learning algorithms are capable of dealing only with numbers. More emphasis will be put on steps such as tokenization, stemming, lemmatization, and stop-word removal. You will also learn about two popular methods for feature extraction: bag of words and Term Frequency-Inverse Document Frequency, as well as various methods for creating new features from existing features. Finally, you will become familiar with how text data can be visualized.