Author

Katie Tedrow
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Senior Global Product Marketing Manager - AI

Katie Tedrow is a Senior Global Product Marketing Manager for AI at SAS, where she focuses on all things Natural Language Processing and Analytics Visualization. She has deep experience in both B2B and B2C marketing within the professional services, tech and financial services industries. Prior to joining SAS, she was a product marketing and digital brand strategy lead at a large financial services company, where she helped to launch the first natural language chatbot for a US bank. Katie holds a bachelor’s degree from North Carolina State University and MBA from the University of Maryland.

Machine Learning
Katie Tedrow 0
The hybrid approach to enhancing your natural language processing

Unlocking the potential of your unstructured text data can lead to great business outcomes but the prospect of starting a new or enhancing your existing Natural Language Processing (NLP) program can feel overwhelming because of the inherently unique (and sometimes messy) nature of human language. Text data doesn’t fit neatly into rows or columns the way that structured data does, which can make it seem more complex to work with. Conversations and written language range from objective statements to subjective perspectives and opinions. The same sentence, depending on its intent and the nuances in how it's said, can have a positive, negative, or neutral sentiment. To get us started, we'll share different types of NLP models used to analyze unstructured data with a focus on the hybrid approach.