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SEUPD@CLEF: Team HIBALL on Incremental Information Retrieval System with RRF and BERT.

Authors: Ceccato A.; Fabbian L.; Huang B. -W.; Khan I. U.; Singh H.; Ferro N.;

SEUPD@CLEF: Team HIBALL on Incremental Information Retrieval System with RRF and BERT.

Abstract

This report showcases the work of the HIBALL team from the University of Padua on Task 1 LongEval-Retrieval of CLEF 2023 [1] [2]. Our goal was to create a general-purpose information retrieval system, using the CLEF document corpus and judgments as our reference. We explored various approaches, including algorithmic techniques and machine learning methods, and compared their results. Our best-performing system, a fusion between classical and AI techniques, shows promising outcomes and may serve as a foundation for future developments.

Country
Italy
Keywords

CLEF 2023; Information Retrieval; Short-term persistence

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green