吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (4): 1242-1252.doi: 10.13229/j.cnki.jdxbgxb201504032

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一种基于LDA的k话题增量训练算法

辛宇1, 杨静1, 谢志强2   

  1. 1.哈尔滨工程大学 计算机科学与技术学院, 哈尔滨 150001;
    2.哈尔滨理工大学 计算机科学与技术学院, 哈尔滨 150080
  • 收稿日期:2013-10-11 出版日期:2015-07-01 发布日期:2015-07-01
  • 通讯作者: 杨静(1962-),女,教授, 博士生导师.研究方向:数据与知识工程.E-mail:yangjing@hrbeu.edu.cn
  • 作者简介:辛宇(1987-),男,博士研究生.研究方向:数据与知识工程.E-mail:xinyu@hrbeu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61370083, 61073043, 61073041, 61370086); 高等学校博士学科点专项科研基金项目(20112304110011,20122304110012)

K-topic increment training algorithm based on LDA

XIN Yu1, YANG Jing1, XIE Zhi-qiang2   

  1. 1.College of Computer Science and Technology, Harbin Engineering University, Harbin 150001,China;
    2.College of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080,China
  • Received:2013-10-11 Online:2015-07-01 Published:2015-07-01

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摘要： 由于LDA模型需要预先给定话题个数k,因此在进行最优话题个数k选取时需要对语料库进行k值循环计算,从而加剧了算法的复杂度。针对LDA模型的最优k值选取问题,提出LDA话题增量训练算法。该方法首先以词-话题概率熵值作为LDA迭代过程中模糊单词的选取标准,并将抽取模糊单词归入新话题;其次,增加LDA变分推理过程中全局参数β(单词-话题概率矩阵)和α(狄利克雷分布参数)的维数及话题个数k;再次,将变换后的全局参数β、α和k作为输入进行变分训练;最后,循环调用LDA话题增量训练算法并在似然函数值收敛时停止循环过程,完成k的增量训练。此外,通过对真实数据集的实验分析验证了本文算法对最优k值选取的有效性和可行性。

关键词: 人工智能, LDA, 变分推理, 增量训练, 话题分类, 自然语言处理

Abstract: As the number of topics k should be given in advance in LDA model, therefore, to choose the optimal number of topics k cyclical calculation is needed, which will increases the complexity of the algorithm. To solve the problem of choosing the optimal topics k, the LDA topic increments training algorithm is proposed. First, the entropy of word-topic probability is taken as the extract standard of fuzzy word in the iterative process, and a new topic is generated by the extracted fuzzy word. Second, the dimension of the global parameter β (word-topic probability matrix), dirichlet parameter α, and number of topics k, are increased in the process of variational inference. Third, the variational training algorithm is executed with the transformed global parameters β, α and k. Finally, the LDA topic increment training algorithm is executed cyclically, which is stopped while the likelihood is converged to complete the increment training of k. The effectiveness and feasibility of the proposed algorithm for optimal topics number k is verified by experimental analysis on real datasets.

Key words: artificial intelltgence, LDA, variational inference, increments training, topic classification, natural language processing

中图分类号: 

  • TP391.4
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