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Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models
Paul Felt
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Kevin Black
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Eric Ringger
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Kevin Seppi
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Robbie Haertel
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Paper Details:
Month: May–June
Year: 2015
Location: Denver, Colorado
Venue:
NAACL |
Citations
URL
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
Paul Felt
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Eric Ringger
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Jordan Boyd-Graber
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Kevin Seppi
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Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA
Paul Felt
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Eric Ringger
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Jordan Boyd-Graber
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Kevin Seppi
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Aggregating and Predicting Sequence Labels from Crowd Annotations
An Thanh Nguyen
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Byron Wallace
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Junyi Jessy Li
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Ani Nenkova
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Matthew Lease
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Comparing Bayesian Models of Annotation
Silviu Paun
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Bob Carpenter
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Jon Chamberlain
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Dirk Hovy
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Udo Kruschwitz
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Massimo Poesio
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http://cs.cmu.edu/
http://mallet.cs.umass.edu
Field Of Study
Task
Corpus Annotation
Approach
Human Computation
Bayesian Model
Generative Model
Discriminative Model
Graphical Model
Language
English
Dataset
News
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