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Active Learning Strategies Based on Text Informativeness - IEEE Xplore
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In this paper, we propose strategies for selecting the next item to label in active learning for text data.
We evaluate the performance of our strategies in two problem settings: the standard active learning setting, where we focus on the improvement of the model.
In this paper, we propose strategies for selecting the next item to label in active learning for text data.
Abstract—In this paper, we propose strategies for selecting the next item to label in active learning for text data. Text data have several text-specific ...
Active Learning Strategies Based on Text Informativeness. Ruide Li (Kyoto University). Yoko Yamakata (The University of Tokyo). Keishi Tajima (Kyoto University).
In this paper we present two very popular aspects in super- vised Machine Learning algorithms: feature selection and active learning paradigm.
Reflective assignments are most effective when the questions encourage deep metacognitive analysis and are only graded based on effort rather than content.
Missing: Informativeness. | Show results with:Informativeness.
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4. Answering questions · Give students a purpose for reading · Focus students' attention on what they are to learn · Help students to think actively as they read ...
Apr 18, 2018 · In this paper we outline several active learning methods for itera- tively modeling text and sampling articles based on model uncertainty with ...
Missing: Informativeness. | Show results with:Informativeness.
Most active learning approaches select either informative or representative unla- beled instances to query their labels. Although several active learning ...