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Mastering Text Mining with R

You're reading from  Mastering Text Mining with R

Product type Book
Published in Dec 2016
Publisher Packt
ISBN-13 9781783551811
Pages 258 pages
Edition 1st Edition
Languages
Concepts
Author (1):
KUMAR ASHISH KUMAR ASHISH
Profile icon KUMAR ASHISH
LinkedIn
Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.
See other products by KUMAR ASHISH

Table of Contents (15) Chapters

Mastering Text Mining with R
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Statistical Linguistics with R 2. Processing Text 3. Categorizing and Tagging Text 4. Dimensionality Reduction 5. Text Summarization and Clustering 6. Text Classification 7. Entity Recognition Index

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

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Authors (1)

Profile icon KUMAR ASHISH
LinkedIn
Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.
Read more
See other products by KUMAR ASHISH

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