Latent Dirichlet Allocation (LDA) and other related topic models are increasingly popular tools for summarization, manifold discovery and other application in discrete data. However, LDA alone does not perform well in IR application. We alleviate it by biased parameter estimation, which makes the topics in LDA more independent than standard LDA. We introduced the background for the biased estimation at first. Then we detailed method for the bias estimation within LDA frame. We reported the result of the biased model in Ad hoc IR experiment showing that the biased estimation outperforms the basic EM method. Finally, the influence of some model parameters was analysis briefly.
展开▼
机译：潜在Dirichlet分配（LDA）和其他相关主题模型是用于汇总，流形发现和其他在离散数据中应用的越来越流行的工具。但是，仅LDA不能在IR应用中发挥良好的作用。我们通过有偏参数估计来缓解这种情况，这使得LDA中的主题比标准LDA更独立。首先，我们介绍了有偏估计的背景。然后，我们详细介绍了LDA帧内偏差估计的方法。我们在Ad hoc IR实验中报告了有偏模型的结果，该结果表明有偏估计的性能优于基本EM方法。最后，简要分析了一些模型参数的影响。
展开▼
