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1 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology Beijing 2 Graduate School of the Chinese Academy of Sciences

Abstract : The content and structure of linked information such as sets of web pages or research paper archives are dynamic and keep on changing. Even though different methods are proposed to exploit both the link structure and the content information, no existing approach can effectively deal with this evolution. We propose a novel joint model, called Link-IPLSI, to combine texts and links in a topic modeling framework incrementally. The model takes advantage of a novel link updating technique that can cope with dynamic changes of online document streams in a faster and scalable way. Furthermore, an adaptive asymmetric learning method is adopted to freely control the assignment of weights to terms and citations. Experimental results on two different sources of online information demonstrate the time saving strength of our method and indicate that our model leads to systematic improvements in the quality of classification.

Keywords : Topic model Link-IPLSI Incremental Learning Adaptive Asymmetric learning





Autor: Huifang Ma - Zhixin Li - Zhongzhi Shi -

Fuente: https://hal.archives-ouvertes.fr/



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