Sample selection for statistical grammar induction
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Interactive information extraction with constrained conditional random fields
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Selective supervision: guiding supervised learning with decision-theoretic active learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Supporting efficient and reliable content analysis using automatic text processing technology
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A cognitive cost model of annotations based on eye-tracking data
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Parallel active learning: eliminating wait time with minimal staleness
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
A semi-supervised word alignment algorithm with partial manual alignments
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
Modeling annotation time to reduce workload in comparative effectiveness reviews
Proceedings of the 1st ACM International Health Informatics Symposium
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We present an empirical investigation of the annotation cost estimation task for active learning in a multi-annotator environment. We present our analysis from two perspectives: selecting examples to be presented to the user for annotation; and evaluating selective sampling strategies when actual annotation cost is not available. We present our results on a movie review classification task with rationale annotations. We demonstrate that a combination of instance, annotator and annotation task characteristics are important for developing an accurate estimator, and argue that both correlation coefficient and root mean square error should be used for evaluating annotation cost estimators.