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)

Feature Extraction from Texts

Let's understand feature extraction with real-life examples. Features represent the characteristics of a person or a thing. These characteristics may or may not uniquely represent a person or a thing. For instance, the general characteristics that a person possesses, such as the number of ears, hands, and legs, are generally not enough to identify that person uniquely. But characteristics such as fingerprints and DNA sequences can be used to recognize that person distinctly. Similarly, in feature extraction, we try to extract attributes from texts that represent those texts uniquely. These attributes are called features. Machine learning algorithms take only numeric features as input. So, it is of utmost importance to represent texts as numeric features. When dealing with texts, we extract both general and specific features. Sometimes, individual words constituting texts do not affect some features directly, such as the language of the text and the...