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nlp:machine-assisted-annotation [2015/05/21 22:53]
plf1
nlp:machine-assisted-annotation [2015/05/21 23:09] (current)
plf1
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 ^ Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models ​  ​^^ ​ ^ Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models ​  ​^^ ​
-[[media:​nlp:​genvsdisc.png]] ​| Paul Felt, Eric Ringger, Kevin Seppi, Kevin Black, Robbie Haertel ​  ​| ​+ | Paul Felt, Eric Ringger, Kevin Seppi, Kevin Black, Robbie Haertel ​  ​| ​
 | :::                             | '''​To appear in NAACL 2015''' ​                                       |  | :::                             | '''​To appear in NAACL 2015''' ​                                       | 
 | :::                             | Crowdsourcing models aggregate multiple fallible human judgments. Previous work largely takes a discriminative modeling approach. This paper demonstrates that a data-aware crowdsourcing model incorporating a generative multinomial data model enjoys a strong competitive advantage over its discriminative log-linear counterpart in the typical crowdsourcing setting. ​ |  | :::                             | Crowdsourcing models aggregate multiple fallible human judgments. Previous work largely takes a discriminative modeling approach. This paper demonstrates that a data-aware crowdsourcing model incorporating a generative multinomial data model enjoys a strong competitive advantage over its discriminative log-linear counterpart in the typical crowdsourcing setting. ​ | 
nlp/machine-assisted-annotation.1432248834.txt.gz ยท Last modified: 2015/05/21 22:53 by plf1
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