H
Hui Yang
Researcher at Georgetown University
Publications - 69
Citations - 1546
Hui Yang is an academic researcher from Georgetown University. The author has contributed to research in topics: Session (computer science) & Web search query. The author has an hindex of 21, co-authored 68 publications receiving 1468 citations. Previous affiliations of Hui Yang include Carnegie Mellon University & National University of Singapore.
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Proceedings ArticleDOI
Structured use of external knowledge for event-based open domain question answering
TL;DR: The approach to perform event-based QA by uncovering the structure within the external knowledge is extended, and the knowledge structure loosely models different facets of QA events, and is used in conjunction with successive constraint relaxation algorithm to achieve effective QA.
Proceedings ArticleDOI
A Metric-based Framework for Automatic Taxonomy Induction
Hui Yang,Jamie Callan +1 more
TL;DR: A novel metric-based framework for the task of automatic taxonomy induction that combines the strengths of both lexico-syntactic patterns and clustering through incorporating heterogeneous features, and reveals the interaction between features and various types of relations.
Proceedings ArticleDOI
Near-duplicate detection by instance-level constrained clustering
Hui Yang,Jamie Callan +1 more
TL;DR: An instance-level constrained clustering approach that incorporates information such as document attributes and content structure into the clustering process to form near-duplicate clusters is presented.
Proceedings ArticleDOI
VideoQA: question answering on news video
TL;DR: The use of multi-modal features, including visual, audio, textual, and external resources, are used to help correct speech recognition errors and to perform precise question answering in news video retrieval.
Proceedings ArticleDOI
Win-win search: dual-agent stochastic game in session search
Jiyun Luo,Sicong Zhang,Hui Yang +2 more
TL;DR: This work mathematically model dynamics in session search, including decision states, query changes, clicks, and rewards, as a cooperative game between the user and the search engine as a dual-agent stochastic game.