潜在Dirichlet分配
主题模型
吉布斯抽样
计算机科学
推论
Dirichlet分布
人工智能
文件分类
自然语言处理
模式识别(心理学)
算法
数学
贝叶斯概率
数学分析
边值问题
作者
Dexin Zhao,Jinqun He,Jin Liu
标识
DOI:10.1109/infoseee.2014.6948100
摘要
Latent Dirichlet Allocation is a classic topic model which can extract latent topic from large data corpus. This model assumes that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. In this paper, we present an algorithm called gLDA for topic text classification by adding topic-category distribution parameter to LDA, which can make the document generated from the most relevant category. Gibbs sampling is employed to conduct approximate inference, and experiment results in two datasets show the effectiveness of this method.
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