How to Robustly Combine Judgements from Crowd Assessors with AWARE

Publikation: Bidrag til tidsskriftKonferenceartikelForskningfagfællebedømt

We propose the Assessor-driven Weighted Averages for Retrieval Evaluation (AWARE) probabilistic framework, a novel methodology for dealing with multiple crowd assessors, who may be contradictory and/or noisy. By modeling relevance judgements and crowd assessors as sources of uncertainty, AWARE directly combines the performance measures computed on the ground-truth generated by the crowd assessors instead of adopting some classification technique to merge the labels produced by them. We propose several unsupervised estimators that instantiate the AWARE framework and we compare them with Majority Vote (MV) and Expectation Maximization (EM) showing that AWARE approaches improve both in correctly ranking systems and predicting their actual performance scores.

TidsskriftCEUR Workshop Proceedings
Sider (fra-til)1DUMMY
StatusUdgivet - 1 jan. 2018
Eksternt udgivetJa
Begivenhed26th Italian Symposium on Advanced Database Systems, SEBD 2018 - Castellaneta Marina (Taranto), Italien
Varighed: 24 jun. 201827 jun. 2018


Konference26th Italian Symposium on Advanced Database Systems, SEBD 2018
ByCastellaneta Marina (Taranto)

ID: 216516836