Text Processing Using NLTK in Python [Video]

More Information
Learn
  • Import, access external corpus & explore frequency distribution of the text in corpus file
  • Learn WordNet usage and a couple of simple application assignments using WordNet
  • Read word & text files and create user-defined corpus
  • Learn HTML parsing using BeautifulSoup
  • Perform tokenization, stemming, lemmatization, spelling corrections, stop words removals, and more
  • Understand Regular expressions for character matching 
  • Write your own Regex tokenizer & stemmer using RNNs
About

Natural Language Processing (NLP) is a feature of Artificial Intelligence concerned with the interactions between computers and human (natural) languages. This course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task.

In this course, you will learn what WordNet is and explore its features and usage. It will teach how to extract raw text from web sources and introduce some critical pre-processing steps. You will also get familiarized with the concept of pattern matching as a way to do text analysis.

By the end of the course, you will be confident & have covered various solutions, covering natural language understanding, Natural Language Processing, and syntactic analysis.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Text-Processing-using-NLTK-in-Python

Style and Approach

This video course takes a solution-based approach where every topic is explicated with the help of a real-world example.

Features
  • Independent solutions that will teach you how to efficiently perform Natural Language Processing in Python
  • Use dictionaries to create your own named entities using this easy-to-follow guide
  • Learn how to implement NLTK for various scenarios with the help of example-rich solutions to take you beyond basic Natural Language Processing
Course Length 1 hour 24 minutes
ISBN 9781789348989
Date Of Publication 29 Apr 2018

Authors

Pratap Dangeti

Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

Krishna Bhavsar

Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining in various industry domains such as hospitality, banking, healthcare, and more. He has worked on many different NLP libraries such as Stanford CoreNLP, IBM's SystemText and BigInsights, GATE, and NLTK to solve industry problems related to textual analysis. He has also worked on analyzing social media responses for popular television shows and popular retail brands and products. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. he recently created an NLP pipeline/toolset and open sourced it for public use. Apart from academics and technology, Krishna has a passion for motorcycles and football. In his free time, he likes to travel and explore. He has gone on pan-India road trips on his motorcycle and backpacking trips across most of the countries in South East Asia and Europe.

Naresh Kumar

Naresh Kumar has more than a decade of professional experience in designing, implementing, and running very-large-scale Internet applications in Fortune Top 500 companies. He is a full-stack architect with hands-on experience in domains such as ecommerce, web hosting, healthcare, big data and analytics, data streaming, advertising, and databases. He believes in open source and contributes to it actively. Naresh keeps himself up-to-date with emerging technologies, from Linux systems internals to frontend technologies. He studied in BITS-Pilani, Rajasthan with dual degree in computer science and economics.