My interests, in no particular order, include Web mining, social networking, query log analysis, web ranking, mass collaboration, online advertising, and online communities. I tend to work within the fields of machine learning and data mining. Most recently, I have been working on natural language processing (e.g., machine reading and semantic parsing). See our Machine Comprehension Test (MCTest), a freely-available set of 660 fictional stories and reading comprehension questions, intended to act as a benchmark for measuring how well a machine understands a short passage of text.
I graduated with a Ph.D in Computer Science from the University of Washington in 2004. My thesis was on learning and inference in collective knowledge bases, a topic I am still quite interested in. I received my Bachelor’s degree in Computer Science from the California Institute of Technology (better known as Caltech) in 1997.
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships from Social MediaEmre Kiciman, Matthew Richardson, in KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM – Association for Computing Machinery, August 1, 2015,
July 24, 2015
Benjamin Van Durme, Luke Zettlemoyer, Matthew Richardson, Scott Yih, and Yan Ke
Microsoft Research, Microsoft, Johns Hopkins University, University of Washington