Data di Pubblicazione:
2023
Abstract:
This is the report based on the work done for LongEval Task 1: Retrieval at CLEF 2023, by team Squid (whose participants are from the University of Padua). Information retrieval (IR) systems have played an increasingly important role in our society and people’s daily lives. Although they have become more and more powerful during the last decades, their temporal persistence is still causing drops in performance, thus failing to achieve good temporal generalisability. To investigate and improve the resolution of this issue, in this paper, we present and discuss the various solutions submitted to the first CLEF 2023 shared task (LongEval-Retrieval), which precisely requires the development of temporal information retrieval systems.
Tipologia CRIS:
04.01 - Contributo in atti di convegno
Keywords:
Information Retrieval; Query Boost; Query Expansion; Search Engine; Word2Vec
Elenco autori:
Cardillo, V.; Dorizza, A.; Maglie, M.; Mameli, D.; Rossi, G.; Russo, M.; Ferro, N.
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Link al Full Text:
Titolo del libro:
CEUR Workshop Proceedings
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