Book Image

Mastering Text Mining with R

By : KUMAR ASHISH
Book Image

Mastering Text Mining with R

By: KUMAR ASHISH

Overview of this book

Text Mining (or text data mining or text analytics) is the process of extracting useful and high-quality information from text by devising patterns and trends. R provides an extensive ecosystem to mine text through its many frameworks and packages. Starting with basic information about the statistics concepts used in text mining, this book will teach you how to access, cleanse, and process text using the R language and will equip you with the tools and the associated knowledge about different tagging, chunking, and entailment approaches and their usage in natural language processing. Moving on, this book will teach you different dimensionality reduction techniques and their implementation in R. Next, we will cover pattern recognition in text data utilizing classification mechanisms, perform entity recognition, and develop an ontology learning framework. By the end of the book, you will develop a practical application from the concepts learned, and will understand how text mining can be leveraged to analyze the massively available data on social media.
Table of Contents (15 chapters)

Chapter 7. Entity Recognition

Extracting information out of unstructured text data is a tedious process, because of the complex nature of natural language. Even after advancements in the field of Natural language processing (NLP), we are far from the point where any unrestricted text can be analyzed and the meaning can be extracted for general purposes. However, if we just focus on a specific set of questions, we can extract a significant amount of information from the text data. Named entity recognition helps identify the important entities in a text, to be able to derive the meaning from the unstructured data. It is a vital component of NLP applications, for example, question-answering systems, product discovery on e-commerce websites, and so on.

In this chapter, we will cover the following topics:

  • Entity extraction

  • Coreference and relationship extraction

  • Sentence boundary detection

  • Named entity recognition