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| Research of Multilingual Author-Topic Model for Profiling Researcher Interests |
| Li Yan, Liu Zhihui, Gao Yingfan |
| Institute of Scientific and Technical Information of China, Beijing 100038 |
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Abstract In the background of big data and globalization, mining latent topics automatically and profiling researchers interests accurately from massive multilingual literature are some of the key issues encountered in providing services with respect to information for knowledge and cross language information retrieval. Currently, the methods adopted to describe researchers interests are mostly based on literatures in one certain language and therefore, these are not applicable to multi-language datasets. This study suggests the JointAT (joint author-topic) model on the basis of author-topic model and multilingual topic model to profile researchers interests from multilingual datasets. Moreover, a Gibbs sampling method to estimate the parameters of the JointAT model is proposed. The experimental results indicate that the JointAT model exhibits a better generalization ability than the author-topic model.
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Received: 26 April 2019
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