III: Small: Improving Information Retrieval by Analysis of Temporal Evidence in a Unified Model

National Science Foundation Award No. 1217279
http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1217279

Internal resources

links

Principal Investigator: 

Miles Efron (http://people.lis.illinois.edu/~mefron)
Graduate School of Library and Information Science
501 E. Daniel St. Champaign, IL 61820


Project dates: October 1, 2012 - September 30, 2016.

Funded Students:

Project Goals

Information retrieval (IR) systems are inherently temporal. Documents change, indexes acquire new documents, and systems answer or "field" queries differently over time. The vision of this project is to capitalize on this temporality to improve the models used for predicting document relevance. The approach is based on a novel probabilistic framework to allow temporal factors to improve IR effectiveness. The framework situates temporality as a key factor in predicting the document relevance. Initial work focuses on established text retrieval settings, estimating document relevance to keyword queries. However, emerging domains such as social media and volunteer-maintained knowledge bases have an inherent temporality that demands new models. Thus, during the project, research pursues problems of filtering and topic evolution. Methods developed in this project will be experimentally evaluated using standard datasets. The project's expected outcome includes improved models and algorithms for retrieving, filtering, and organizing textual data that arrives incrementally over time.

The project will benefit society in two ways. IR systems play a key role in people's daily information use. This project will advance the public's ability to negotiate an increasingly complex information landscape, because the expected outcomes will improve search engine technology. Research results will be disseminated primarily via academic conferences and journals. Work will also be stored in an archival institutional repository, affording the public long-term access to results. Progress and general information about the project will be published on the project Web site (http://timer.lis.illinois.edu). The project will provide research experience for students and will advance scientific education. In addition, course materials will be developed to support on-line teaching of information retrieval to non-technical students.

Research Challenges

Current Results, Publications, Presentations, etc.

  1. M. Efron. Query Representation for Cross-Temporal Information Retrieval. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013), pages 383-392, Dublin, Ireland, 2013.
  2. M. Efron, J. Deisner, P. Organisciak, G. Sherman, and A. Lucic. The University of Illinois' Graduate School of Library and Information Science at TREC 2012. In Proceedings of the Twenty-First Text REtrieval Conference (TREC 2012), Gaithersburg, Maryland, 2012.
  3. M. Efron, J. Lin, J. He, and A. de Vries. Temporal Feedback for Tweet Search with Non-Parametric Density Estimation. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), pages 33-42, Gold Coast, Australia, 2014.
  4. M. Efron, C. Willis, P. Organisciak, B. Balsamo, and A. Lucic. The University of Illinois' Graduate School of Library and Information Science at TREC 2013. In Proceedings of the Twenty-Second Text REtrieval Conference (TREC 2013), Gaithersburg, Maryland, 2013.
  5. M. Efron, C. Willis, and G. Sherman. Learning sufficient queries for entity filtering. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), pages 1091-1094, Gold Coast, Australia, 2014.
  6. J. Lin and M. Efron. Temporal Relevance Profiles for Tweet Search. In Proceedings of the SIGIR 2013 Workshop on Time-Aware Information Access (TAIA 2013), Dublin, Ireland, 2013.
  7. J. Lin and M. Efron. Overview of the TREC-2013 Microblog Track. In Proceedings of the Twenty-Second Text REtrieval Conference (TREC 2013), Gaithersburg, Maryland, 2013.
  8. J. Lin and M. Efron. Evaluation as a Service for Information Retrieval. In ACM SIGIR Forum, 47(2):8-14, 2013.
  9. J. Lin and M. Efron. Infrastructure Support for Evaluation as a Service. In Proceedings of the 23rd International World Wide Web Conference Companion (WWW 2014), pages 79-83, Seoul, South Korea, 2014.
  10. J. Lin, M. Efron, Y. Wang, and G. Sherman. Overview of the TREC-2014 Microblog Track. In Proceedings of the Twenty-Third Text REtrieval Conference (TREC 2014), Gaithersburg, Maryland, 2014.
  11. J. Lin, M. Efron, Y. Wang, G. Sherman, and E. Voorhees. Overview of the TREC-2015 Microblog Track, Gaithersburg, Maryland, 2014.
  12. J. Rao, J. Lin, and M. Efron. Reproducible experiments on lexical and temporal feedback for tweet search. In Advances in Information Retrieval: 37th European Conference on IR Research, ECIR 2015 Vienna, Austria, March 29 - April 2, 2015 Proceedings, pages 755-767, Vienna, Austria, 2015.
  13. G. Sherman, M. Efron, and C. Willis. The University of Illinois’ Graduate School of Library and Information Science at TREC 2014. In Proceedings of the Twenty-Third Text REtrieval Conference (TREC 2014), Gaithersburg, Maryland, 2014.
  14. E. M. Voorhees, J. Lin, and M. Efron. On Run Diversity in "Evaluation as a Service". Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2014), pages 959-962, Gold Coast, Australia, 2014.
  15. Y. Wang, G. Sherman, J. Lin, and M. Efron. Assessor Differences and User Preferences in Tweet Timeline Generation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2015), pages 615-624, Santiago, Chile, 2015.
  16. C. Willis, G. Sherman, M. Efron. What Makes a Query Temporally Sensitive? Proceedings of the 2016 Annual Meeting of the Association for Information Science and Technology (ASIS&T 2016), Copenhager, Denmark, October, 2016.
  17. C. Willis, G. Sherman, and M. Efron. 2016. What Makes a Query Temporally Sensitive?. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR '16). ACM, New York, NY, USA, 1065-1068


This material is based upon work supported by the National Science Foundation under Grant No. 1217279.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.