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)

Topic Discovery

The main goal of topic modeling is to find a set of topics that can be used to classify a set of documents. These topics are implicit because we do not know what they are beforehand, and they are unnamed. We just generally assume that some documents are similar to each other and that we can organize them into topics.

The number of topics is usually small; that is, from 2 to 10. However, there are some use cases in which you may want to have up to 100 (or even more) topics. Since it is the computer algorithm that discovers the topics, the number is generally arbitrary. These topics may not always directly correspond to topics a human would identify. In practice, the number of topics should be much smaller than the number of documents. This helps the topic modeling algorithm in the sorting process. The more examples of documents that we provide, the better the accuracy with which the algorithm can sort and place the documents into categories.

The number of topics...