Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances

By Burr Settles. 2011

216 citations recorded by the source corpus.

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Citations in the corpus

Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020, background): “Existing approaches focus on passive learning only and do not consider the interaction between the user and the learner [7, 8, 9], whereas, interactive learning frameworks such as active [10] and coactive learning [11] do not consider the issue of trust.”
Rationale-Augmented Convolutional Neural Networks for Text Classification (2016, methodology): “Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 795–804, Austin, Texas, November 1-5, 2016. c©2016 Association for Computational Linguistics”
Explanatory Interactive Machine Learning (2019, background): “More recently, researchers explored feature supervision (Raghavan and others 2006; 2007; Druck and others 2008; 2009; Settles 2011; Attenberg and others 2010) and rationales (Zaidan and others 2007; 2008; Sharma and others 2015), which leverage both label- and feature-level (or sentence-level, for r”
Flock: Hybrid Crowd-Machine Learning Classifiers (2015, background): “, [40]), or allowing for fast switching between programming and evaluating example attributes [35].”
Deploying an interactive machine learning system in an evidence-based practice center: abstrackr (2012, background): “Nonetheless, there are questions regarding its eﬃcacy in practice [3, 7, 2].”
Toward interactive training and evaluation (2011, methodology): “Settles [22] develops an interface for interactive training in which users can label both instances and features.”
Active Learning (2012, methodology): “1: User interface for the DUALIST system (Settles, 2011).”
Natural language processing applied to mental illness detection: a narrative review (2022, background): “Semi-supervised learning 176 incorporates few labeled data and large amounts of unlabeled data into the training process, which can be used to facilitate annotation 177 or improve classi ﬁ cation performance when labeled data is scarce.”
A Framework and Tool for Collaborative Extraction of Reliable Information (2013, background): “It should also be noted that while standard classifiers are trained using the document labels U , it is also possible to directly label the features φn (Melville et al., 2009; Settles, 2011).”