贝叶斯概率LSA模型权重更新算法

Bayesian probability weight update algorithm of LSA model

针对潜在语义分析(LSA)模型的权重更新问题,提出了一种基于贝叶斯理论的自适应权重更新算法ALSAB。ALSAB采用最大后验概率估计与期望值最大(EM)算法对概率LSA模型参数进行有效的估计,在充分考虑多次更新中不常用字词概率参数降低问题的前提下,采用增量学习方法降低多次更新产生的累积效应。实验结果表明,与现有的权重更新算法相比,提出的ALSAB算法显著地提高了检索的准确率与召回率。

To the weight update of Latent Semantic Analysis（LSA） model,this paper proposes an adaptive weight update algorithm based on Bayesian theory（ALSAB）.ALSAB adopts Maximum A Posteriori（MAP） provability estimation and Expectation-Maximization（EM） algorithm to estimate the weight parameters of LSA,and ALSAB employs incremental learning to decrease accumulative effect caused in continuous update with considering that the probability of uncommon words decreases in continuous update.Experimental results show that,compared with the existing algorithms,the proposed ALSAB algorithm greatly improves recall and precision rates of information retrieval systems.