Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances
This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e.g., words) and instances (e.g., documents). We present a novel semi-supervised training algorithm developed for this setting, which is (1) fast enough to support real-time interactive speeds, and (2) at least as accurate as preexisting methods for learning with mixed feature and instance labels. Human annotators in user studies were able to produce near-state-of-the-art classifiers—on several corpora in a variety of application domains—with only a few minutes of effort.
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Active LearningInformation ExtractionSentiment AnalysisText ClassificationSimilar Papers 제목 키워드 기반
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