Earticle

초록

영어

A large number of electronic documents are labeled using human-interpretable annotations. High-efficiency text mining on such data set requires generative model that can flexibly comprehend the significant of observed labels while simultaneously uncovering topics within unlabeled documents. This paper presents a novel and generalized on-line labeled topic model based on global and local topic (GL-OLT) tracking the time evolution of topics in a sequentially organized multi-labeled corpus. GL-OLT topic model has an incrementally update principle based on time slices by an on-line fashion, and each label has not only a set of local topics, but also has several global topics. Empirical results are presented to demonstrate significant improvements accuracy of label predictive, and lower perplexity and high performance of our proposed model when compared with other models.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1. Modeling Documents with Topics
  2.2. GL-OLT Model
 3. Approximate Variation Inference
 4. Experiments
  4.1. Perplexity for Different Models
  4.2. Classification Accuracy
  4.3. Algorithmic Efficiency
 5. Summary
 References

키워드

저자

• YongHeng Chen [ College of Computer Science, Minnan Normal University, zhangzhou 363000, China ] Response author
• Yaojin Lin [ College of Computer Science, Minnan Normal University, zhangzhou 363000, China ]
• Hao Yue [ College of Computer Science, Minnan Normal University, zhangzhou 363000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605