Learning More with Less: Reducing Annotation Effort with Active and Interactive Learning
نویسندگان
چکیده
Annotation learning is an important task for many kinds of text analysis. Statistical machine learning techniques have been developed to learn annotation models over labeled data that are then used to annotate unlabeled data. One of the major bottlenecks of a conventional machine learning approach is getting sufficient pre-labeled training data: annotating text is a time consuming, tedious and error prone process. Moreover, as all the training examples are not equally informative or equally easy to annotate, it is beneficial to identify examples that would help the model to converge with minimal user annotation effort.
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