Semantic pattern tree kernels for short-text classification

Kwanho Kim, Beom Suk Chung, Ye Rim Choi, Jonghun Park

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

4 Scopus citations

Abstract

Kernel methods are widely used for document classification in diverse domains. Popular kernels such as bag-of-word kernels and tree kernels show satisfactory results in classifying documents such as articles, e-mails or web pages. However, they provide less satisfactory performances in classifying short-text documents since the short documents have insufficient feature space. In order to cope with the problem, this paper presents a novel kernel function called semantic pattern tree kernel for classifying short-text documents. The proposed kernel extends the feature space of each document by incorporating syntactic and semantic information using three levels of semantic annotations. Experiments on the Open Directory Project dataset show that in classifying short-text documents the semantic pattern tree kernels achieve higher accuracy than the conventional kernels.

Original languageEnglish
Title of host publicationProceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011
Pages1250-1252
Number of pages3
DOIs
StatePublished - 2011
Event9th IEEE Int. Conf. on Dependable, Autonomic and Secure Comput., DASC 2011, incl. 9th Int. Conf. on Pervasive Intelligence and Computing, PICom 2011, 9th Int. Symp. on Embedded Computing, EmbeddedCom 2011, 1st Int. Conf. on Cloud and Green Comput.CGC - Sydney, NSW, Australia
Duration: 12 Dec 201114 Dec 2011

Publication series

NameProceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011

Conference

Conference9th IEEE Int. Conf. on Dependable, Autonomic and Secure Comput., DASC 2011, incl. 9th Int. Conf. on Pervasive Intelligence and Computing, PICom 2011, 9th Int. Symp. on Embedded Computing, EmbeddedCom 2011, 1st Int. Conf. on Cloud and Green Comput.CGC
Country/TerritoryAustralia
CitySydney, NSW
Period12/12/1114/12/11

Keywords

  • document classification
  • kernel methods
  • open directory project
  • semantic
  • short-text document
  • support vector machine

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