F
Fabio Crestani
Researcher at University of Lugano
Publications - 373
Citations - 7426
Fabio Crestani is an academic researcher from University of Lugano. The author has contributed to research in topics: Relevance (information retrieval) & Ranking (information retrieval). The author has an hindex of 40, co-authored 365 publications receiving 6237 citations. Previous affiliations of Fabio Crestani include University UCINF & University of Glasgow.
Papers
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Book ChapterDOI
Spoken versus Written Queries for Mobile Information Access
Heather Du,Fabio Crestani +1 more
TL;DR: In this paper, it is shown that users would speak more words when issuing their queries due to the ease of speech when they are enabled to form queries via voice to an information retrieval system than forming queries in written form.
Book ChapterDOI
Temporal Analysis of Comparative Opinion Mining
TL;DR: This study shows that temporal analysis of comparative opinion mining provides more current and relevant information to users compared to standard opinion mining.
Book
Advances in information retrieval theory : Third International Conference, ICTIR 2011, Bertinoro, Italy, September 12-14, 2011 : proceedings
TL;DR: This book constitutes the refereed proceedings of the Third International Conference on the Theory of Information Retrieval, ICTIR 2011, held in Bertinoro, Italy, in September 2011 and contains 25 revised full papers and 13 short papers presented together with the abstracts of two invited talks.
Journal ArticleDOI
Report on ECIR 2016: 38th European Conference on Information Retrieval
Nicola Ferro,Fabio Crestani,Marie-Francine Moens,Josiane Mothe,Fabrizio Silvestri,Jaana Kekäläinen,Paolo Rosso,Paul Clough,Gabriella Pasi,Christina Lioma,Stefano Mizzaro,Giorgio Maria Di Nunzio,Claudia Hauff,Omar Alonso,Pavel Serdyukov,Gianmaria Silvello +15 more
TL;DR: This report summarizes the conference in terms of the presented keynotes, scientific and social programme, industry day, tutorials, workshops and student support.
Proceedings ArticleDOI
A Collection for Detecting Triggers of Sentiment Spikes
TL;DR: A collection of tweets that can be used for detecting and ranking the likely triggers of sentiment spikes towards different entities and can be useful for further research on detecting sentiment change triggers, sentiment analysis and sentiment prediction.