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


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.
Research Challenges
Broad challenges include:
  • Improving ad hoc information retrieval by mining temporal information latent in document collections
  • Classifying queries with respect to temporal valence
  • Topic representation during long-term filtering, especially during the task of knowledge base acceleration.

Read more at the project site.

Project PI(s)

PI: Miles Efron
Project Contact: Miles Efron
Funded by: National Science Foundation
Grant number: 1217279

Research Area(s)

Socio-technical Data Analytics
Faculty, researchers and students in the Socio-technical Data Analytics Group design, develop, and evaluate new technologies in order to better understand the dynamic interplay between information, pe…

Project Team