Aside from POS, one of the most common labeling problems is finding entities in the text. Typically NER constitutes name, location, and organizations. There are NER systems that tag more entities than just three of these. The problem can be seen as a sequence, labeling the Named entities using the context and other features. There is a lot more research going on in this area of NLP where people are trying to tag Biomedical entities, product entities in retail, and so on. Again, there are two ways of tagging the NER using NLTK. One is by using the pre-trained NER model that just scores the test data, the other is to build a Machine learning based model. NLTK provides the ne_chunk() method and a wrapper around Stanford NER tagger for Named Entity Recognition.
NLTK Essentials
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NLTK Essentials
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Overview of this book
<p>Natural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it’s becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool.</p>
<p>You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text.</p>
<p>By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.</p>
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Table of Contents (17 chapters)
NLTK Essentials
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Introduction to Natural Language Processing
Text Wrangling and Cleansing
Part of Speech Tagging
Parsing Structure in Text
NLP Applications
Text Classification
Web Crawling
Using NLTK with Other Python Libraries
Social Media Mining in Python
Text Mining at Scale
Index
Customer Reviews