大规模文本数据的主题建模

LI Xi-ming; OUYANG Ji-hong; LU You

doi:10.1631/FITEE.1400352

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大规模文本数据的主题建模

计算机与自动化 | Updated：2022-06-06

- 大规模文本数据的主题建模
- Topic modeling for large-scale text data
- 信息与电子工程前沿（英文） 2015年16卷第6期页码：457-465
- Affiliations：
  
  1.College of Computer Science and Technology, Jilin University, Changchun 130012, China
  2.MOE Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun 130012, China
- Author bio：
  
  [ "Xi-ming LI, E-mail:liximing86@gmail.com" ]
  [ "Ji-hong OUYANG, E-mail:ouyj@jlu.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61170092, 61133011, and 61103091)
- DOI：10.1631/FITEE.1400352
  中图分类号： TP391.1
- 收稿：2014-10-15，
  
  修回：2015-05-07，
  
  纸质出版：2015-06
- Accepted：
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大规模文本数据的主题建模[J]. 信息与电子工程前沿（英文）, 2015,16(6):457-465.

LI Xi-ming, OUYANG Ji-hong, LU You. Topic modeling for large-scale text data[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 457-465.
大规模文本数据的主题建模[J]. 信息与电子工程前沿（英文）, 2015,16(6):457-465. DOI： 10.1631/FITEE.1400352.

LI Xi-ming, OUYANG Ji-hong, LU You. Topic modeling for large-scale text data[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(6): 457-465. DOI： 10.1631/FITEE.1400352.

摘要

目的

研究大规模数据的主题模型在线推理算法，针对随机变分推理算法中随机梯度误差较大的问题，提出一种移动平均随机变分推理算法。

创新点

使用多次迭代的随机梯度移动平均值近似代替真实随机梯度，以此减小随机梯度和真实梯度间的误差。

方法

以主题模型的基础模型潜在狄利克雷分配为载体展开研究。考虑不同次迭代的文本子集具有不同的词汇（表1），使用不同次迭代的随机项移动平均值近似代替真实随机梯度的随机项。为尽可能保证算法的精度，使用最近

次迭代的随机项（图2）并验证所提算法的收敛性。

结论

在随机变分推理算法基础上，提出一种移动平均随机变分推理算法，实现更好的文本主题建模效果和更快的收敛速度。

Abstract

This paper develops a novel online algorithm

namely moving average stochastic variational inference (MASVI)

which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named 'stochastic variational inference' and 'SGRLD'

our algorithm achieves a faster convergence rate and better performance.

关键词

Keywords

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