Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Chapter 6. Working with Text - Natural Language Processing and Information Retrieval

In the previous two chapters, we covered the basics of machine learning: we spoke about supervised and unsupervised problems.

In this chapter, we will take a look at how to use these methods for processing textual information, and we will illustrate most of our ideas with our running example: building a search engine. Here, we will finally use the text information from the HTML and include it into the machine learning models.

First, we will start with the basics of natural language processing, and implement some of the basic ideas ourselves, and then look into efficient implementations available in NLP libraries.

This chapter covers the following topics:

  • Basics of information retrieval
  • Indexing and searching with Apache Lucene 
  • Basics of natural language processing
  • Unsupervised models for texts - dimensionality reduction, clustering, and word embeddings
  • Supervised models for texts - text classification and learning...