Affiliations: [a] School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| [b] Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| [c] Key Laboratory of Data Storage System, Ministry of Education, Huazhong University of Science and Technology, Wuhan, China
| [d] INRIA Rennes, Rennes, France
| [e] College of Science, Wuhan University of Science and Technology, Wuhan, China
Correspondence:
[*]
Corresponding author. Yi Yin, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China. E-mail: yinyi1023@sina.com.
Abstract: One of the core content of the prediction is that, on the basis of text label attributes, we can use the algorithm and a heuristic approach to acquire the association of texts, and extract the available text for the user. Therefore, this paper proposes a new content. First, the multi-label attributes are chosen to be the feature structure of text, and it is given the classification and assignment according to the distinguish method of the statistical data. Second, considering the relation between texts, we improve the traditional maximum entropy method. We are able to control the number of multiple leading text and subsequent text at the same time. Our method makes stronger association of text, and it leads to a more unified direction and higher correlation of obtained text through the label attributes. Then we can predict the similar texts. Experiments show that with the consideration of multi-label attributes of text and the control of the number of leading text as well as the subsequent text, the recall rate and precision are definitely improved when compared to similar existing methods.
Keywords: Text prediction, maximum entropy model, leading text, subsequent text, label attribute
