Text analytics, Predictive analytics, Machine learning

Machine learning provides powerful and cost effective means to organize and reason over large amounts of textual data. The challenge, however, is that business requirements along with the data often change rapidly making it difficult to curate high-quality training sets. This challenge is more pronounced in the case of micro-blogs such as Twitter where each tweet contains very little signal. This presentation is a case study of training high quality micro-blog classifiers by leveraging existing external knowledge sources to automatically create training sets.

As a natural language processing (NLP) expert, Nick Pendar applies machine learning and data mining techniques to textual data in order to classify, extract and organize information from a variety of sources. Nick received his Ph.D. from the University of Toronto in 2005, and in the same year started an academic position at Iowa State University, where he conducted and directed research on NLP and text categorization for various educational and legal purposes. Prior to joining Skytree as a Senior Data Scientist, Nick also held engineering and R&D positions at Groupon, Uptake and H5. He has published papers and given numerous talks on the topic of NLP to a variety of audiences for over 15 years; he has also filed multiple patents, and is an active member of several related professional organizations and conferences.