R
Rogier Brussee
Researcher at Utrecht University
Publications - 25
Citations - 668
Rogier Brussee is an academic researcher from Utrecht University. The author has contributed to research in topics: Semantics & Thesaurus (information retrieval). The author has an hindex of 13, co-authored 24 publications receiving 640 citations. Previous affiliations of Rogier Brussee include Novay.
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Proceedings ArticleDOI
Topic Detection by Clustering Keywords
Christian Wartena,Rogier Brussee +1 more
TL;DR: Evaluation on Wikipedia articles shows that clusters of keywords correlate strongly with the Wikipedia categories of the articles, and a newly proposed term distribution taking co-occurrence of terms into account gives best results.
Proceedings ArticleDOI
Finding the story: broader applicability of semantics and discourse for hypermedia generation
Lloyd Rutledge,Martin Alberink,Rogier Brussee,Stanislav Pokraev,William van Dieten,Mettina Veenstra +5 more
TL;DR: This paper presents the results of the first phase of the Topia project, which explored generating a discourse structure derived from generic processing of the underlying domain semantics, transforming this to a structured progression and then using this to steer the choice of hypermedia communicative devices used to convey the actual information in the resulting presentation.
Proceedings ArticleDOI
Keyword Extraction Using Word Co-occurrence
TL;DR: The results show that using word co-occurrence information can improve precision and recall over tf.idf, and some alternative relevance measures that do use relations between words are studied.
Personalized Museum Experience: The Rijksmuseum Use Case
TL;DR: The overall goal of the project is to explore different users' characteristics and personalize users' museum experiences within the Rijksmuseum virtual and physical collections.
Proceedings ArticleDOI
Using Tag Co-occurrence for Recommendation
TL;DR: A second order co-occurrence and a related distance measure measure for tag similarities that is robust against the variation in tags is introduced that can derive methods to analyze user interest and compute recommendations.