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Development and use of a gold-standard data set for subjectivity classifications
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Reliability measurement without limits
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MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
That's nice... what can you do with it?
Computational Linguistics
From annotator agreement to noise models
Computational Linguistics
Learning with annotation noise
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
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NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
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ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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Many interesting phenomena in conversation can only be annotated as a subjective task, requiring interpretative judgements from annotators. This leads to data which is annotated with lower levels of agreement not only due to errors in the annotation, but also due to the differences in how annotators interpret conversations. This paper constitutes an attempt to find out how subjective annotations with a low level of agreement can profitably be used for machine learning purposes. We analyse the (dis)agreements between annotators for two different cases in a multimodal annotated corpus and explicitly relate the results to the way machine-learning algorithms perform on the annotated data. Finally we present two new concepts, namely 'subjective entity' classifiers resp. 'consensus objective' classifiers, and give recommendations for using subjective data in machine-learning applications.