Dr. Nishant Chandra has driven machine learning and natural language innovation in BFSI, e-commerce, R&D, and mobile telecom industries in USA and India. He developed and implemented natural language predictive models that are deployed in top banks and telecom companies resulting in significant impact across their value chain.
For his contributions, Dr. Chandra was acknowledged as one of the top 10 data scientists in India. He has received the prestigious Barrier Fellowship and several other awards and marks of recognition. The Department of Homeland Security, United States Government, has classified Dr. Chandra as an outstanding researcher.
He was the conference session chair for the GSPx conference at San Jose, California. He has been a reviewer for IEEE transactions, served on the editorial board of the Human Language Technology conference, and spoken at several international conferences. He also has five assigned patents and several journal and conference publications. Dr. Chandra is a passionate puzzler who invents puzzles and has represented India in the World Puzzle Championship at Stamford, Connecticut. He received his Ph.D. in Electrical and Computer Engineering from Mississippi State University.
Hierarchical Natural Language Representation Using Deep Learning
Deep learning has created a revolution in the natural language processing domain and corporations are leveraging it in various ways. The technology barrier is significantly reduced with open source technologies that are easy to configure and use. Several generic open source tools are available in machine learning, including deep learning, which can be customized for natural language processing. This presentation will help the audience to go beyond generic NLP problem solving by leveraging deep learning, customizing it for their industry. Specifically, they’ll learn that:
♦ Sentiment doesn’t have to be positive, negative or neutral but it can be extracted from the conversation
♦ Summarization doesn’t have to be entire document but only certain context
♦ Text classification doesn’t have to be exactly text/phrase/spelling based but can also include variation of acronym and synonym
♦ NLP can be applied broadly, and complex use cases can be built through intelligent iteration on simple examples.