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nlp:machine-assisted-annotation [2015/05/21 16:09]
plf1
nlp:machine-assisted-annotation [2015/05/21 16:53]
plf1
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 == Publications == == Publications ==
  
-Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models + 
-Paul Felt, Eric Ringger, Kevin Seppi, Robbie Haertel +Early Gains Matter: A Case for Preferring Generative over Discriminative Crowdsourcing Models ​  ^^  
-'''​To appear in NAACL 2015'''​ +| [[media:​nlp:​genvsdisc.png]] | Paul Felt, Eric Ringger, Kevin Seppi, Kevin Black, Robbie Haertel ​  |  
-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.  ​+| :::                             ​| ​'''​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.  ​
  
 ^ [http://​www.lrec-conf.org/​proceedings/​lrec2014/​pdf/​1153_Paper.pdf| MOMRESP: A Bayesian Model for Multi-Annotator Document Labeling] ​  ​^^ ​ ^ [http://​www.lrec-conf.org/​proceedings/​lrec2014/​pdf/​1153_Paper.pdf| MOMRESP: A Bayesian Model for Multi-Annotator Document Labeling] ​  ​^^ ​
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 | [[media:​nlp:​120px-boxplot.png]] | Paul Felt, Eric K. Ringger, Kevin D. Seppi, Robbie Haertel, Kristian Heal, Deryle Lonsdale ​  ​| ​ | [[media:​nlp:​120px-boxplot.png]] | Paul Felt, Eric K. Ringger, Kevin D. Seppi, Robbie Haertel, Kristian Heal, Deryle Lonsdale ​  ​| ​
 | :::                             | '''​ LREC 2012 ''' ​                                       |  | :::                             | '''​ LREC 2012 ''' ​                                       | 
-| :::                             | We investigate how good machine assistance needs to be in order to actually ​helping ​human annotators (in terms of time and cost). | +| :::                             | We investigate how good machine assistance needs to be in order to actually ​help human annotators (in terms of time and cost) for the task of Syriac morphological disambiguation. | 
  
  
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 | :::                             | '''​ EMNLP 2010 ''' ​                                       |  | :::                             | '''​ EMNLP 2010 ''' ​                                       | 
 | :::                             | We design a hierarchical probabilistic model to perform morphological analysis of an under-resourced Semitic language. This model achieves 86.7% accuracy, a 29.7% reduction in error rate over reasonable baselines. |  | :::                             | We design a hierarchical probabilistic model to perform morphological analysis of an under-resourced Semitic language. This model achieves 86.7% accuracy, a 29.7% reduction in error rate over reasonable baselines. | 
-| :::                             | Extended version: [http://​contentdm.lib.byu.edu/​cdm/​singleitem/​collection/​ETD/​id/​2226/​rec/​1 Peter McClanahan'​s M.S. Thesis |  
  
  
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-^ [http://​facwiki.cs.byu.edu/​nlp/​index.php/​Workshop_on_Active_Learning_for_NLP| NAACL HLT 2009 Workshop on Active Learning for NLP]   ^^  +^ [http://​facwiki.cs.byu.edu/​nlp/​index.php/​Workshop_on_Active_Learning_for_NLP| NAACL HLT 2009 Workshop on Active Learning for NLP]   ^ 
-| :::                             | '''​ Organized by: Eric Ringger, Robbie Haertel, Katrin Tomanek ''' ​                                       | +| '''​ Organized by: Eric Ringger, Robbie Haertel, Katrin Tomanek ''' ​                                       | 
  
  
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- 
-<​!-- ​ 
-==Want to help?== 
- 
-We are currently annotating a corpus of English news articles. ​ You can help by taking the time to annotate a set of sentences. ​ You will be presented with one sentence at a time and will be asked to either annotate a single word or the entire sentence. ​ We are ready for user help.  We expect that the average participant will spend less than an hour on the task.  Thank you in advance for participating! 
- 
-<​b><​big>​[http://​nlp.cs.byu.edu/​alfaUserStudy/​ Begin the study now! ]</​big></​b>​ 
- 
-==Get Updates== 
- 
-For updates on the status of the study and results from the study, please [http://​groups.google.com/​group/​alfa-userstudy/​subscribe subscribe to the Google Group]. 
---> 
  
 ==Questions?​== ==Questions?​==
  
 Please contact [http://​faculty.cs.byu.edu/​~ringger/​ Eric Ringger] or [http://​faculty.cs.byu.edu/​~kseppi/​ Kevin Seppi], or visit the Natural Language Processing research lab in room 3346 TMCB. Please contact [http://​faculty.cs.byu.edu/​~ringger/​ Eric Ringger] or [http://​faculty.cs.byu.edu/​~kseppi/​ Kevin Seppi], or visit the Natural Language Processing research lab in room 3346 TMCB.
nlp/machine-assisted-annotation.txt · Last modified: 2015/05/21 17:09 by plf1
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