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Mayank Kejriwal

About Me

Dr. Mayank Kejriwal is a research lead at the University of Southern California’s Information Sciences Institute, and a research assistant professor at USC’s Department of Industrial and Systems Engineering.  He has delivered talks, tutorials, demonstrations and workshops at over 20 international academic and industrial venues, published more than 30 peer-reviewed articles and papers, and currently co-authoring two books on knowledge graphs. In 2018, he was awarded a Key Scientific Challenge Award by the Allen Institute for Artificial Intelligence and was designated a Forbes under 30 Scholar. 

Measuring without labels: a different approach to information extraction

Information extraction is a major problem in the fields of natural language processing and web mining, in particular when it comes to evaluating domains where language cannot be taken at face value. In modern artificial intelligence (AI) community, information extraction is done using machine learning. Supervised machine learning methods take training set of webpages, with gold standard extractions, and learn an IE function based on statistical models like conditional random fields and even deep neural nets.

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