K
Kira Radinsky
Researcher at Technion – Israel Institute of Technology
Publications - 77
Citations - 2806
Kira Radinsky is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Computer science & Ranking (information retrieval). The author has an hindex of 22, co-authored 69 publications receiving 2383 citations. Previous affiliations of Kira Radinsky include Microsoft.
Papers
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Proceedings ArticleDOI
A word at a time: computing word relatedness using temporal semantic analysis
TL;DR: This paper proposes a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information in word semantics as a vector of concepts over a corpus of temporally-ordered documents.
Proceedings ArticleDOI
Learning causality for news events prediction
TL;DR: A new methodology for modeling and predicting such future news events using machine learning and data mining techniques is presented, and the Pundit algorithm generalizes examples of causality pairs to infer a causality predictor.
Patent
Realtime multiple engine selection and combining
TL;DR: In this article, the authors present an architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity.
Proceedings ArticleDOI
Mining the web to predict future events
Kira Radinsky,Eric Horvitz +1 more
TL;DR: Methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories are described and the predictive power of the approach on real-world events withheld from the system is evaluated.
Proceedings ArticleDOI
Modeling and predicting behavioral dynamics on the web
TL;DR: A temporal modeling framework adapted from physics and signal processing that can be used to predict time-varying user behavior using smoothing and trends and a novel learning algorithm that explicitly learns when to apply a given prediction model among a set of such models.