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Eugene Agichtein
Researcher at Emory University
Publications - 166
Citations - 11564
Eugene Agichtein is an academic researcher from Emory University. The author has contributed to research in topics: Question answering & Web search query. The author has an hindex of 47, co-authored 166 publications receiving 10917 citations. Previous affiliations of Eugene Agichtein include Amazon.com & Microsoft.
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Proceedings ArticleDOI
Web Question Answering: Beyond Factoids: SIGIR 2015 Workshop
Eugene Agichtein,David Carmel,Charles L. A. Clarke,Praveen Paritosh,Dan Pelleg,Idan Szpektor +5 more
TL;DR: This workshop aims to explore the boundaries of Web question answering to better understand the spectrum of approaches and possible responses that are more detailed than a short fact, yet are more useful than a full document result.
Proceedings Article
Proceedings of the 2008 ACM workshop on Search in social media
TL;DR: This workshop is to bring together academic and industry researchers in information retrieval and social media to consider the following questions: How should the authors search in social media?
Proceedings ArticleDOI
De-Biased Modeling of Search Click Behavior with Reinforcement Learning
Jianghong Zhou,Sayyed M. Zahiri,Simon Hughes,Khalifeh Al Jadda,Surya Kallumadi,Eugene Agichtein +5 more
TL;DR: In this article, the authors proposed the De-biased Reinforcement Learning Click model (DRLC), which relaxes previously made assumptions about the users' examination behavior and resulting latent states, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.
Proceedings ArticleDOI
Workshop on health search and discovery: helping users and advancing medicine
TL;DR: This workshop brings together researchers and practitioners from industry and academia to discuss search and discovery in the medi-cal domain, and how to make medical and health information more accessible to laypeople, as evidenced in query streams and other sources such as social media.
Proceedings ArticleDOI
Mining query structure from click data: a case study of product queries
TL;DR: The main contribution of this work is a novel approach to query segmentation based on unsupervised machine learning, which can be used to search product databases more accurately, and improve result presentation and query suggestion.