  Active learning is a state-of-art machine learning approach to deal with an
abundance of unlabeled data. In the field of Natural Language Processing,
typically it is costly and time-consuming to have all the data annotated. This
inefficiency inspires out our application of active learning in text
classification. Traditional unsupervised k-means clustering is first modified
into a semi-supervised version in this research. Then, a novel attempt is
applied to further extend the algorithm into active learning scenario with
Penalized Min-Max-selection, so as to make limited queries that yield more
stable initial centroids. This method utilizes both the interactive query
results from users and the underlying distance representation. After tested on
a Chinese news dataset, it shows a consistent increase in accuracy while
lowering the cost in training.
