ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking

By Gianluca Demartini, Djellel Eddine Difallah, Philippe Cudré-Mauroux. The Web Conference, 2012

513 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/20d3c2e8f9f5440b34a12dadb14e33defde587ca

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In Artificial Intelligence

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In Management Science and Operations Research

Citations in the corpus

SURF: improving classifiers in production by learning from busy and noisy end users (2020, methodology): “We will compare SURF to two groups of crowdsourcing algorithms: GROUP 1: Dawid-Skene (DS) [1], iBCC [3], EBCC [6], LFC [8]) and GROUP 2: ZenCrowd (ZC) [2], CATD [5], GLAD [12], Majority Vote (MV) ) For each instantiation of the parameters we vary we run the experiments 10 times, resampling N = 1000 ”
Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets (2017, background): “However, despite its efﬁciency, researchers have also reported difﬁculty obtaining high quality labels using crowdsourcing [3, 16].”
Toward Annotator Group Bias in Crowdsourcing (2021, unknown): “We compare our proposed framework GroupAnno with nine existing true label inference methods (Zheng et al., 2017), including majority voting (MV), ZenCrowd (Demartini et al., 2012), Mini-max (Zhou et al., 2012), LFC-binary (Raykar et al., 2010), CATD (Li et al., 2014a), PM-CRH (Aydin et al., 2014), K”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, methodology): “This work has focused primarily on combining machine learning methods with crowdsourcing; crowdsourcing is typically used for verifying predicted matches [189, 68, 85]; in some cases, it is also used to provide training data for matching algorithms [138, 192, 85].”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “For the purpose of simplicity, many studies [60, 62,75,92,93,119,121] suppose that the knowledge base contains all the mapping entities for all the entity mentions, and thus ignore the unlinkable problem of entity mentions.”
Crowdsourced Data Management: A Survey (2016, background): “Graph-based methods [22], [26], [28], [64], [112], [136], [137].”
Corleone: hands-off crowdsourcing for entity matching (2014, background): “Current works use the crowd to verify predicted matches [30, 31, 6], finds the best questions to ask the crowd [33], and finds the best UI to pose such questions [34].”
Evaluating the Quality of Learning Resources: A Learnersourcing Approach (2021, background): “Most of the state-of-the-art consensus approaches developed within crowdsourcing community rely on black-box machine learning algorithms (e.g., [37], [65]) for aggregating crowd-sourced decisions.”
Truth Inference in Crowdsourcing: Is the Problem Solved? (2017, methodology): “The single-choice tasks are especially studied in [34, 16, 15, 53, 41, 61, 35, 30, 27, 46, 5].”