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1
Using a Bayesian Model to Combine LDA Features with Crowdsourced Responses
Simpson, Edwin
;
Reece, Steven
;
Penta, Antonio
;
Ramchurn, Sarvapali
In: DTIC (2013)
Abstract:
This paper describes a crowdsourcing system that integrates machine learning techniques with human classifiers, showing how to apply a Bayesian approach to classifier combination to the challenge of crowdsourcing document topic labels. First, we use a number of NLP techniques to extract informative document features. We then screen and select workers using Amazon Mechanical Turk to label selected documents. We then apply Independent Bayesian Classifier Combination (IBCC) to classify the complete set of documents in a semi-supervised manner, taking into account the unreliability of crowd-sourced labels. More documents are then selected intelligently for labeling by the crowd. We demonstrate superior results using IBCC compared to a two-stage classifier and strong performance with only 16% documents labelled by the crowd. ; Presented at the Text REtrieval Conference (TREC 2012) (21st) held in Gaithersburg, Maryland on 6-9 November 2012. The conference was co-sponsored by the National Institute of Standards and Technology (NIST) the Defense Advanced Research Projects Agency (DARPA) and the Advanced Research and Development Activity (ARDA).
Keyword:
*LEARNING MACHINES
;
*NATURAL LANGUAGE
;
BAYES THEOREM
;
CROWDSOURCING
;
HUMAN CLASSIFIERS
;
IBCC(INDEPENDENT BAYESIAN CLASSIFIER COMBINATION)
;
INFORMATION RETRIEVAL
;
Information Science
;
Linguistics
;
NLP(NATURAL LANGUAGE PROCESSING)
;
SYMPOSIA
URL:
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA580905
http://www.dtic.mil/docs/citations/ADA580905
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