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New Technology of Library and Information Service  2013, Vol. 29 Issue (2): 30-35    DOI: 10.11925/infotech.1003-3513.2013.02.05
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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.
Key wordsSemi-supervised learning      Text classification      Short text      Self-training     
Received: 27 January 2013      Published: 24 April 2013
:  TP391.1  

Cite this article:

Zhang Qian, Liu Huailiang. An Algorithm of Short Text Classification Based on Semi-supervised Learning. New Technology of Library and Information Service, 2013, 29(2): 30-35.

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