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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:REGENTS OF THE UNIVERSITY OF MINNESOTA
Doing Business As Name:University of Minnesota-Twin Cities
PD/PI:
  • Brent J Hecht
  • (847) 467-5103
  • bhecht@northwestern.edu
Award Date:08/24/2015
Estimated Total Award Amount: $ 248,277
Funds Obligated to Date: $ 0
  • FY 2015=$0
Start Date:09/01/2015
End Date:12/31/2016
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CHS: Small: Collaborative Research: Human-Centered Semantic Relatedness
Federal Award ID Number:1526988
DUNS ID:555917996
Parent DUNS ID:006220594
Program:Cyber-Human Systems (CHS)
Program Officer:
  • William Bainbridge
  • (703) 292-8930
  • wbainbri@nsf.gov

Awardee Location

Street:200 OAK ST SE
City:Minneapolis
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Minnesota - Twin Cities
Street:200 Union Street SE
City:Minneapolis
State:MN
ZIP:55455-2070
County:Minneapolis
Country:US
Cong. District:05

Abstract at Time of Award

This work addresses key open questions about semantic relatedness (SR) measures, a family of algorithms used throughout computer science and related fields that help computers replicate human assessments of the relatedness between two concepts. Decades of research and development have transformed SR measures into a critical component of a wide swath of intelligent technologies in areas ranging from information retrieval to human-computer interaction to spatial computing. However, despite the importance and ubiquity of SR, researchers have only recently begun to examine it from a human-centered perspective. These human-centered studies have problematized key assumptions underlying the entire SR literature, e.g. that people from all cultural contexts agree on a single relatedness value between any two concepts. This project addresses long-overdue open questions in the SR literature that will move the field towards a more human-centered approach to SR. To do so, this research will collect datasets of relatedness judgments, mine patterns in Wikipedia and other content, perform statistical analyses, create and evaluate algorithms, develop software, and conduct large-scale user studies. First, this research will complete four threads of work that redefine semantic relatedness to address human-centered concerns raised in the SR literature: (1) investigate the role of culture in SR and use these results to redefine SR to incorporate cultural context, (2) study SR among the low-notability concepts that are critical to end users but entirely ignored by the SR literature, (3) address the need for SR measures that explain their relatedness estimates to users, and (4) develop more robust human-centered SR evaluation procedures and support their adoption through easy-to-use software. Second, this research will develop new conceptual representations for SR measures that accommodate differing cultural perspectives and create compact contextual SR models that empower applications with tractable human-centered SR algorithms. Finally, the research will demonstrate the power of human-centered SR approaches through their application in improved recommender systems, enhanced Wikipedia reader experiences, and novel information discovery tools.

Publications Produced as a Result of this Research

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Johnson, I., Lin, Y., Li, T., Hall, A., Halfaker, A., Schöning, J., and Hecht, B. "Not at Home on the Range: Peer Production and the Urban/Rural Divide." ACM SIGCHI 2016, v., 2016, p..

Johnson, I., Sengupta, S., Schöning, J., and Hecht, B. "The Geography and Importance of Localness in Geotagged Social Media." ACM SIGCH 2016, v., 2016, p..

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