基于SLDTM的主题提取方法

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东北电力大学学报 ›› 2017, Vol. 37 ›› Issue (5) : 80-86.

信息·计算机·自动化

基于SLDTM的主题提取方法

郭晓利, 周自岚

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Topic Extraction Method Based on SLDTM

Guo Xiaoli, Zhou Zilan

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摘要

针对主题提取时现有的LDA模型对于主题数目和关键时间点的确定存在一定困难、对于主题结果的准确解释上存在难度的问题，本文提出的SLDTM融合了一种改进的聚类算法到DTM模型中，并在各个子集上采用标签信息进行监督学习。该模型中滑动窗口大小依据主题分布特征而变化，实现更合理的文本集分割，主题的个数也可变且易于理解。实验表明：和以往主题模型相比，SLDTM提取的主题更能体现内容发生的重要变化，语义也更加清晰。

Abstract

Owing to exist the difficult of determine the number of topics and key point of times and accurate interpretation of topics for existing LDA model.There present SLDTM,which fused an improved clustering algorithm to the DTM model and using the tag information for supervised learning in each subset.In this paper,a more reasonable text set segmentation can be achieved because the sliding window size of SLDTM can be changed according to the distribution characteristics of the topics.The number of topics is variable and can be understand easier.experimental results show that compared with the previous topic model,these extracted topics of SLDTM can reflect the important changes of the content and the semantics is clearer.

导出引用

郭晓利, 周自岚. 基于SLDTM的主题提取方法. 东北电力大学学报. 2017, 37(5): 80-86

Guo Xiaoli, Zhou Zilan. Topic Extraction Method Based on SLDTM. Journal of Northeast Electric Power University. 2017, 37(5): 80-86

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2017-05-12	2017-10-30
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