论文名称：LDA-based document models for cross language information retrieval
论文类型：期刊论文
发表刊物：Journal of Computational Information Systems
收录刊物：EI、Scopus
卷号：9
期号：16
页面范围：6343-6350
ISSN号：15539105
摘要：This paper proposes a bilingual biomedical topic space model in which both Chinese and English abstract are represented by improved Latent Dirichlet Allocation with SVD and NMF decomposition smoothing methods. The improved LDA-based method is used to calculate the relationship between query and document and combine the results of different matrix factorization. A set of different dimension models is set up, with the help of which we can achieve bilingual cross-language indexing. The experimental results show that our method can improve the retrieval accuracies effectively. ? 2013 Binary Information Press.
发表时间：2013-08-15