SLDA-TC: A Novel Text Categorization Approach Based on Supervised Topic Model

doi:10.3969/j.issn.0372-2112.2019.06.017

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SLDA-TC: A Novel Text Categorization Approach Based on Supervised Topic Model

更新时间：2025-07-02

- SLDA-TC: A Novel Text Categorization Approach Based on Supervised Topic Model
- Acta Electronica Sinica Vol. 47, Issue 6, Pages: 1300-1308(2019)
- 作者机构：
  
  1. 山东工商学院计算机科学与技术学院,山东,烟台,264005
  2. 山东省高等学校协同创新中心:未来智能计算,山东,烟台,264005
  3. 山东省高校智能信息处理重点实验室山东工商学院,山东,烟台,264005
  4. 大连海事大学信息科学技术学院,辽宁,大连,116026
  5. 山东工商学院计算机科学与技术学院,山东,烟台,264005
  6. 山东省高等学校协同创新中心:未来智能计算,山东,烟台,264005
  7. 山东省高校智能信息处理重点实验室山东工商学院,山东,烟台,264005
  8. 大连海事大学信息科学技术学院,辽宁,大连,116026
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61175053, No.61472227, No.61773244, No.61602277, No.61772319);Research Project of Colleges and Universities in Shandong Province (No.J18KA385)
- DOI：10.3969/j.issn.0372-2112.2019.06.017
  CLC： TP181
- Published：2019
- 稿件说明：
移动端阅览
SLDA-TC: A Novel Text Categorization Approach Based on Supervised Topic Model[J]. Acta Electronica Sinica, 2019, 47(6): 1300-1308.
DOI：

SLDA-TC: A Novel Text Categorization Approach Based on Supervised Topic Model[J]. Acta Electronica Sinica, 2019, 47(6): 1300-1308. DOI： 10.3969/j.issn.0372-2112.2019.06.017.

摘要

本文提出了一种有监督主题模型的SLDA-TC（Supervised LDA-Text Categorization）文本分类方法，引入主题-类别概率分布参数，识别主题-类别的语义信息；提出SLDA-TC-Gibbs主题采样新方法，对每个词的隐含主题采样，只从该词所在文档的同类其它文档中采样，并给出了理论推导；另外，其主题数只需略大于类别数.实验表明，对比LDA-TC（LDA-Text Categorization）和SVM算法，本方法能提高分类精度和时间性能.

Abstract

In this paper

SLDA-TC

a novel text categorization model based on supervised topic model is proposed.The new parameter represents the probability distribution of topic-category is introduced.The SLDA-TC-Gibbs sampling algorithm is presented.At each iteration

a word's latent topic sampling only utilizes the other training documents having the same category with the document the word occurred

meanwhile

the theoretical proof is given.In the SLDA-TC model

the number of topics is only slightly larger than the number of categories.The experimental results demonstrate that the SLDA-TC model promotes the accuracy and speed for text classification compared with the LDA-TC and SVM algorithms.

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Related Institution

Key Laboratory of Intelligent Information Processing in Universities of Shandong(Shandong Technology and Business University) Shandong Yantai China

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College of Information and Electronic Engineering, Shandong Technology and Business University

Key Laboratory of Intelligent Information Processing in Universities of Shandong(Shandong Technology and Business University)

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