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)

Entity extraction


The process of extracting information from unstructured documents is called information extraction. In today's world, most of the data produced over the internet is semi-structured or unstructured; this data is mostly in a human-understandable format, what we call natural language, so most of the time, natural language processing comes into play during information extraction. Entity recognition is a sub process in the chain of information extraction process. NER is one of the important and vital parts of the information extraction process. NER is sometimes also called entity extraction or entity chunking .The main job of NER is to extract the rigid designators in the document and classify these elements in the text to a predefined category. The named entity extractor has a set of predefined categories such as the following:

  • persons

  • organizations

  • locations

  • time

  • money

  • percentages

  • dates

Given an unstructured document, NER will annotate the block or extract the relevant features. Consider...