On aggregating labels from multiple crowd workers to infer relevance of documents

Mehdi Hosseini*, Ingemar J. Cox, Nataša Milić-Frayling, Gabriella Kazai, Vishwa Vinay

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

48 Citations (Scopus)

Abstract

We consider the problem of acquiring relevance judgements for information retrieval (IR) test collections through crowdsourcing when no true relevance labels are available. We collect multiple, possibly noisy relevance labels per document from workers of unknown labelling accuracy. We use these labels to infer the document relevance based on two methods. The first method is the commonly used majority voting (MV) which determines the document relevance based on the label that received the most votes, treating all the workers equally. The second is a probabilistic model that concurrently estimates the document relevance and the workers accuracy using expectation maximization (EM). We run simulations and conduct experiments with crowdsourced relevance labels from the INEX 2010 Book Search track to investigate the accuracy and robustness of the relevance assessments to the noisy labels. We observe the effect of the derived relevance judgments on the ranking of the search systems. Our experimental results show that the EM method outperforms the MV method in the accuracy of relevance assessments and IR systems ranking. The performance improvements are especially noticeable when the number of labels per document is small and the labels are of varied quality.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 34th European Conference on IR Research, ECIR 2012, Proceedings
Pages182-194
Number of pages13
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event34th European Conference on Information Retrieval, ECIR 2012 - Barcelona, Spain
Duration: 1 Apr 20125 Apr 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference34th European Conference on Information Retrieval, ECIR 2012
Country/TerritorySpain
CityBarcelona
Period1/04/125/04/12

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