Short text classification based on strong feature thesaurus

Bing-kun WANG; Yong-feng HUANG; Wan-xia YANG; Xing LI

doi:10.1631/jzus.C1100373

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Short text classification based on strong feature thesaurus

Computer & Automation | Updated：2022-12-09

- Short text classification based on strong feature thesaurus
- Short text classification based on strong feature thesaurus
- Frontiers of Information Technology & Electronic Engineering Vol. 13, Issue 9, Pages: 649-659(2012)
- Affiliations：
  
  1.Information Cognitive and Intelligent System Research Institute, Department of Electronic and Engineering, Tsinghua University, Beijing 100084, China
  2.Information Technology National Laboratory, Tsinghua University, Beijing 100084, China
- Author bio：
  
  [ "Bing-kun WANG, E-mail: Wangbingkun77@yahoo.com.cn; wbk10@mails.tsinghua.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.C1100373
  CLC： TP391.4
- Received：19 December 2011，
  
  Revised：2012-;8-;3，
  
  Published：2012-09
- Accepted：
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Bing-kun WANG, Yong-feng HUANG, Wan-xia YANG, et al. Short text classification based on strong feature thesaurus[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 649-659.
DOI：

Bing-kun WANG, Yong-feng HUANG, Wan-xia YANG, et al. Short text classification based on strong feature thesaurus[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(9): 649-659. DOI： 10.1631/jzus.C1100373.

摘要

Abstract

Data sparseness

the evident characteristic of short text

has always been regarded as the main cause of the low accuracy in the classification of short texts using statistical methods. Intensive research has been conducted in this area during the past decade. However

most researchers failed to notice that ignoring the semantic importance of certain feature terms might also contribute to low classification accuracy. In this paper we present a new method to tackle the problem by building a strong feature thesaurus (SFT) based on latent Dirichlet allocation (LDA) and information gain (IG) models. By giving larger weights to feature terms in SFT

the classification accuracy can be improved. Specifically

our method appeared to be more effective with more detailed classification. Experiments in two short text datasets demonstrate that our approach achieved improvement compared with the state-of-the-art methods including support vector machine (SVM) and Nave Bayes Multinomial.

关键词

Keywords

references

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