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)

Why Vector Representations?

Computers natively understand 1s and 0s. Even the text displayed on computer screens is encoded in some numeric form. To make the processing easy, text is encoded as numbers. For the field of NLP, the demand is even more onerous. Here the computers are being taught to read, listen, and understand natural language. Mathematical functions are also being applied to the text data with the goal of detecting patterns.

NLP algorithms require large volumes of text data. However, the processing of this data takes a huge amount of time and eventually affects the performance of the algorithm. Thus, in order to make the processing faster and performance reasonable, we can take advantage of data structures. By representing data as vectors, we allow CPUs to operate over data in batches, which in turn improves performance. This is another key reason for representing text as vectors.


The process of converting data into a specified format is called encoding...