Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition

Xia Cui, Sadamori Kojaku, Naoki Masuda, Danushka Bollegala

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Feature sparseness is a problem common to cross-domain and short-text classification tasks. To overcome this feature sparseness problem, we propose a novel method based on graph decomposition to find candidate features for expanding feature vectors. Specifically, we first create a feature-relatedness graph, which is subsequently decomposed into core-periphery (CP) pairs and use the peripheries as the expansion candidates of the cores. We expand both training and test instances using the computed related features and use them to train a text classifier. We observe that prioritising features that are common to both training and test instances as cores during the CP decomposition to further improve the accuracy of text classification. We evaluate the proposed CP-decomposition-based feature expansion method on benchmark datasets for cross-domain sentiment classification and short-text classification. Our experimental results show that the proposed method consistently outperforms all baselines on short-text classification tasks, and perform competitively with pivot-based cross-domain sentiment classification methods.

Original languageEnglish
Title of host publicationNAACL HLT 2018 - Lexical and Computational Semantics, SEM 2018, Proceedings of the 7th Conference
EditorsMalvina Nissim, Jonathan Berant, Alessandro Lenci
PublisherAssociation for Computational Linguistics (ACL)
Pages255-264
Number of pages10
ISBN (Electronic)9781948087223
Publication statusPublished - 2018
Externally publishedYes
Event7th Joint Conference on Lexical and Computational Semantics, SEM 2018, co-located with NAACL HLT 2018 - New Orleans, United States
Duration: 2018 Jun 52018 Jun 6

Publication series

NameNAACL HLT 2018 - Lexical and Computational Semantics, SEM 2018, Proceedings of the 7th Conference

Conference

Conference7th Joint Conference on Lexical and Computational Semantics, SEM 2018, co-located with NAACL HLT 2018
Country/TerritoryUnited States
CityNew Orleans
Period18/6/518/6/6

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

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