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Abstract: The typical paradigm of the machine learning task is to obtain a large amount of labeled data and then train a classifier for further predictions/classifications. However, this paradigm may suffer from the high cost and low efficiency in manual annotation. In this talk, we use Name Entity Recognition (NER) task as an example to show how distant supervision and weak supervision can be applied to reduce human annotation. We introduce the pattern-enhanced NER methods, which automatically mines the entity naming principles to enhance the weak supervision.