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Title:Cost-effective learning for classifying human values
Author(s):Ishita, Emi; Fukuda, Satoshi; Oga, Toru; Tomiura, Yoichi; Oard, Douglas W.; Fleischmann, Kenneth R.
Subject(s):Text classification
Content analysis
Human values
Annotation cost
Abstract:Prior work has found that classifier accuracy can be improved early in the process by having each annotator label different documents, but that later in the process it becomes better to rely on a more expensive multiple-annotation process in which annotators subsequently meet to adjudicate their differences. This paper reports on a study with a large number of classification tasks, finding that the relative advantage of adjudicated annotations varies not just with training data quantity, but also with annotator agreement, class imbalance, and perceived task difficulty.
Issue Date:2020-03-23
Publisher:iSchools
Series/Report:iConference 2020 Proceedings
Genre:Conference Poster
Type:Text
image
Language:English
URI:http://hdl.handle.net/2142/106554
Rights Information:Copyright 2020 Emi Ishita, Satoshi Fukuda, Toru Oga, Yoichi Tomiura, Douglas W. Oard, and Kenneth R. Fleischmann
Date Available in IDEALS:2020-03-17


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