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)

Building Pipelines for NLP Projects

What does the word pipeline refer to? In general, pipeline refers to a structure that allows a streamlined flow of air, water, or something similar. In this context, pipeline has a similar meaning. It helps to streamline various stages of an NLP project.

An NLP project is done in various stages, such as tokenization, stemming, feature extraction (tf-idf matrix generation), and model building. Instead of carrying out each stage separately, we create an ordered list of all these stages. This list is known as a pipeline. Let's solve a text classification problem using a pipeline in the next section.

Exercise 38: Building Pipelines for NLP Projects

In this exercise, we will develop a pipeline that will allow us to create a TF-IDF matrix representation from sklearn's fetch_20newsgroups text dataset. Follow these steps to implement this exercise:

  1. Open a Jupyter notebook.
  2. Insert a new cell and add the following code to import...