A topic detection method based on Semantic Dependency Distance and PLSA

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Topic detection is a hot topic in the field of text mining. In this paper, focusing on the Chinese interactive text, we explored a novel topic detection method, named SDD-PLSA, which integrates Semantic Dependency Distance (SDD) and PLSA. It not only has the advantages of PLSA, which is an efficient, effective method and is widely used in text mining, but also considers the semantic and syntax information. Thus, the problem of lacking semantic information in PLSA can be avoided. SDD-PLSA has two main steps. The first is using SDD to classify the sentences that have a high similarity in semantics into several groups according to semantic feature extraction of the interactive text. Then, a PLSA classifier is used upon the result of the first step. The experiments show that the accuracy of detection on love topic has been improved to 64.8% when using SDD-PLSA, better than 55.4% when using PLSA.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2012
Pages703-708
Number of pages6
DOIs
StatePublished - 2012
Event2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2012 - Wuhan, China
Duration: 23 May 201225 May 2012

Publication series

NameProceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2012

Conference

Conference2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2012
Country/TerritoryChina
CityWuhan
Period23/05/1225/05/12

Keywords

  • E-learning
  • Interactive Text
  • PLSA
  • Semantic Dependency Distance
  • Topic Detection

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