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An Algorithm of Short Text Classification Based on Semi-supervised Learning |
Zhang Qian, Liu Huailiang |
School of Economics and Management, Xidian University, Xi'an 710071, China |
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Abstract According to the characteristics of short texts and the bottleneck problem of annotation in dealing with large numbers of unlabeled samples, traditional algorithms of text classification can not be used directly. This paper introduces a method of short text classification based on semi-supervised learning and builds a semi-supervised classification model. It is feasible to accomplish the self-training of the training samples and takes full advantages of the unlabeled parts of training texts by using the initial classifier. The bottleneck problem of annotation is solved and the good performance of classifier is shown. The contrast experiment shows that the algorithm of short text classification based on semi-supervised learning can get better classified effect.
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Received: 27 January 2013
Published: 24 April 2013
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