Sponsor
Machine Learning
Co-Sponsor(s)
Computer Science; Electrical and Computer Engineering (ECE); Energy Initiative; Information Initiative at Duke (iiD); Mathematics; Social Science Research Institute (SSRI); Statistical Science
MLBytes Series: Sequence Tagging in NLP
Thursday, September 17,
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Speaker(s): Abstract: Sequence Tagging is an important problem in natural language processing where words or phrases are classified into predefined entity groups. The output of such models can be used in a variety of applications including document tagging,…
Abstract: Sequence Tagging is an important problem in natural language processing where words or phrases are classified into predefined entity groups. The output of such models can be used in a variety of applications including document tagging, search, and product review analysis. Neural network based architectures to tackle the sequence tagging problem will be discussed as well as open source libraries that can be utilized in your own sequence tagging tasks.
John Bralich is currently a data scientist at Infinia ML where he applies machine learning to solve business problems. Prior to this, he worked for a Durham based startup as a machine learning engineer. There he applied machine learning techniques to perform medical document analysis with the goal of creating more patient-friendly medical reports. He received his Bachelor's and Master's in Electrical and Computer Engineering from Duke University.
Please register here: https://duke.zoom.us/j/98352539624
John Bralich is currently a data scientist at Infinia ML where he applies machine learning to solve business problems. Prior to this, he worked for a Durham based startup as a machine learning engineer. There he applied machine learning techniques to perform medical document analysis with the goal of creating more patient-friendly medical reports. He received his Bachelor's and Master's in Electrical and Computer Engineering from Duke University.
Please register here: https://duke.zoom.us/j/98352539624
