Deep Learning for Natural Language Processing

Chapter 2

Applications of Natural Language Processing

Learning Objectives

By the end of this chapter, you will be able to:

• Describe POS tagging and its applications
• Differentiate between rule-based and stochastic POS taggers
• Perform POS tagging, chunking, and chinking on text data
• Perform named entity recognition for information extraction
• Develop and train your own POS tagger and named entity recognizer
• Use NLTK and spaCy to perform POS tagging, chunking, chinking, and named entity recognition

This chapter aims to introduce you to the plethora of applications of NLP and the various techniques involved within.

Introduction

This chapter begins with a quick recap of what natural language processing is and what services it can help provide. ...