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- W2297694736 abstract "Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA." @default.
- W2297694736 created "2016-06-24" @default.
- W2297694736 creator A5060505838 @default.
- W2297694736 date "2014-11-10" @default.
- W2297694736 modified "2023-10-18" @default.
- W2297694736 title "Modeling Word Relatedness in Latent Dirichlet Allocation" @default.
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- W2297694736 doi "https://doi.org/10.48550/arxiv.1411.2328" @default.
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