Machine Learning on Natural Language Text and Log Data

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About the Technology Brief

Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. Machine learning can be utilized to analyze both unstructured text data and semi- structured log data to provide you with the insights needed to achieve your business goals.

About the Author

Nick Pendar is a Natural Language Processing (NLP) Data Scientist at Skytree. As an NLP expert, he 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 NLP 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 14 years; he has also filed multiple patents, and is an active member of several related professional organizations and conferences.

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