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  The paper, " Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization." written by MSIIP members Zhiyang He, Ji Wu, Tao Li, was accepted by IEEE Transactions on Emerging Topics in Computing.

  The abstruct of this paper is attached below:

  Multi-label categorization, which is more difficult but practical than the conventional binary and multi-class categorization, has received a great deal of attention in recent years. This paper proposes a novel probabilistic generative model, label correlation mixture model (LCMM), to depict the multiply labeled documents, which can be used for multi-label spoken document categorization as well as multi-label text categorization. In LCMM, labels and topics have the one-to-one correspondences. LCMM consists of two important components: a label correlation model and a multi-label conditioned document model. The label correlation model formulates the generating process of labels where the dependencies between the labels are taken into account. We also propose an efficient algorithm for calculating the probability of generating an arbitrary subset of labels. The multi-label conditioned document model can be regarded as a supervised label mixture model, in which labels for a document are known. Each label is characterized by distributions over words. For the parameter learning of the multi-label conditioned document model, in addition to maximum likelihood estimation (MLE), a discriminative approach based on the minimum classification error rate training (MCE) is proposed. To evaluate LCMM, extensive multi-label categorization experiments are conducted on a spoken document data set and three standard text data sets. The experimental results in comparison with other competitive methods demonstrate the effectiveness of LCMM.

 

 
     
 
 

Tsinghua University  |  School of Information Science and Technology  |  Department of Electronic Engineering  |  USTC iFLYTEK

 
 

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