The Application of Active Query K-Means in Text Classification

• URL: http://arxiv.org/abs/2107.07682v1
• Date: Fri, 16 Jul 2021 03:06:35 GMT
• Title: The Application of Active Query K-Means in Text Classification
• Authors: Yukun Jiang
• Abstract summary: Active learning is a state-of-art machine learning approach to deal with an abundance of unlabeled data. Traditional unsupervised k-means clustering is first modified into a semi-supervised version in this research. A novel attempt is applied to further extend the algorithm into active learning scenario with Penalized Min-Max-selection. After tested on a Chinese news dataset, it shows a consistent increase in accuracy while lowering the cost in training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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