document: Reducing the human overhead in text categorization Many applications in text processing require significant human effort for either labeling large document collections ( when learning statistical models ) or extrapolating rules from them ( when using knowledge engineering ) . In this work , we describe away to reduce this effort , while retaining the methods ' accuracy , by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning . Using a standard sentiment-classification dataset and real customer feedback data , we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy . Moreover , the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used .
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