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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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APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
Ali Ahmadvand,Sayyed M. Zahiri,Simon Hughes,Khalifeh Al Jadda,Surya Kallumadi,Eugene Agichtein +5 more
TL;DR: Zhang et al. as mentioned in this paper proposed a novel deep neural model named attentive pseudo relevance feedback network (APRF-Net) to enhance the representation of rare queries for query categorization, which can be used to select the relevant fine-grained product categories in a product taxonomy.
Proceedings ArticleDOI
VPW: an interactive prototype of a web-based visual paired comparison cognitive diagnostic test
TL;DR: This demonstration shows an early prototype of a Web-based version of the VPC task, the Viewport Viewing task (VPW), that requires only a computer with a mouse, and has the potential to extend the accessibility of cognitive diagnostics, allowing for Web- based behavioral screening for aMCI to be deployed world-wide, without requiring any special-purpose equipment.
Proceedings Article
Towards Automatic Question Answering over Social Media by Learning Question Equivalence Patterns
TL;DR: An unsupervised method for automatically learning question equivalence patterns from CQA archive data that can be used to match new questions to their equivalents that have been answered before, and thereby help suggest answers automatically.
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DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search.
TL;DR: DeepCAT as discussed by the authors proposes a deep learning model, which learns joint word-category representations to enhance the query understanding process and improves the performance of category mapping on minority classes, tail and torso queries.
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Learning to Focus when Ranking Answers.
TL;DR: A novel ranking algorithm for question answering is proposed, QARAT, which uses an attention mechanism to learn on which words and phrases to focus when building the mutual representation of the query-answer pair.