In this session hosted by NASA's Interagency Implementation and Advanced Concepts Team (IMPACT), Sukriti Sharma from IBM presents some of IBM’s best natural language processing (NLP) technologies productionized and available in a Python embeddable library. ‘Watson NLP’ is IBM’s standard NLP library, with a wide range of features with pre-trained models and support for custom training. It is cross-lingually stable across several languages (30+) and powers 10+ IBM products, supporting both high quality and high runtime performance use cases and containing innovations from IBM research and other IBM products. Sukriti provides a broad overview of Watson NLP and deeper insights on entity extraction, text classification, and topic modeling.
Presenter Bio
Sukriti Sharma is a machine learning (ML) engineer at IBM. He manages the team building a python embeddable standard NLP library, and his focus has been on experimenting with different NLP algorithms, particularly for entity extraction; model evaluation, analysis and data collection; building scalable machine learning solutions and productionizing NLP. Sukriti has a master’s degree in computer science from North Carolina State University, with a specialization in data science.