Search Engines represents an important application of Information Retrieval. In particular, a major branch of Search Engines is devoted to web search. In this document we summarize our work to produce a submission for the CLEF LongEval initiative [1], primarily concerning web search. The described activity first focuses onto the development of an indexing and searching IR system with the best possible performance based on the provided training data then evaluates its performance on test data coming from different scenarios. We first introduce the task and related problems. Subsequently we present the retrieval systems that we have used for the program submission. Afterwards, we discuss the results obtained with the various systems and compare them in the training scope to explain why some systems perform better than others. Finally, metrics analysis is extended to the additional scenarios LongEval focuses on, along with statistical considerations over the systems’ output.

SEUPD@CLEF: Team DARDS - IR System for Short and Long Term Retrieval / Carlesso, Daniel; Gobbo, Riccardo; Merlo, Simone; Pomaro, Angela; Spinosa, Diego; Ferro, Nicola. - 3497:(2023), pp. 2338-2367. (Intervento presentato al convegno CLEF2023 Conference and Labs of the Evaluation Forum tenutosi a Thessaloniki; Greece).

SEUPD@CLEF: Team DARDS - IR System for Short and Long Term Retrieval

Gobbo Riccardo
Secondo
;
Pomaro Angela
;
2023

Abstract

Search Engines represents an important application of Information Retrieval. In particular, a major branch of Search Engines is devoted to web search. In this document we summarize our work to produce a submission for the CLEF LongEval initiative [1], primarily concerning web search. The described activity first focuses onto the development of an indexing and searching IR system with the best possible performance based on the provided training data then evaluates its performance on test data coming from different scenarios. We first introduce the task and related problems. Subsequently we present the retrieval systems that we have used for the program submission. Afterwards, we discuss the results obtained with the various systems and compare them in the training scope to explain why some systems perform better than others. Finally, metrics analysis is extended to the additional scenarios LongEval focuses on, along with statistical considerations over the systems’ output.
2023
CLEF2023 Conference and Labs of the Evaluation Forum
CLEF 2023; LongEval; Spam detection; Translation; Synonyms; Reranking; Boosting; Query Expansion; Document Expansion; ANOVA
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
SEUPD@CLEF: Team DARDS - IR System for Short and Long Term Retrieval / Carlesso, Daniel; Gobbo, Riccardo; Merlo, Simone; Pomaro, Angela; Spinosa, Diego; Ferro, Nicola. - 3497:(2023), pp. 2338-2367. (Intervento presentato al convegno CLEF2023 Conference and Labs of the Evaluation Forum tenutosi a Thessaloniki; Greece).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1698224
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