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        <title>Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition</title>
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        <namePart type="given">Xia</namePart>
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    <name type="personal">
        <namePart type="given">Sadamori</namePart>
        <namePart type="family">Kojaku</namePart>
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    <name type="personal">
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        <namePart type="family">Masuda</namePart>
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    <name type="personal">
        <namePart type="given">Danushka</namePart>
        <namePart type="family">Bollegala</namePart>
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            <title>Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics</title>
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            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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    <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.</abstract>
    <identifier type="citekey">cui-etal-2018-solving</identifier>
    <identifier type="doi">10.18653/v1/S18-2030</identifier>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition
%A Cui, Xia
%A Kojaku, Sadamori
%A Masuda, Naoki
%A Bollegala, Danushka
%Y Nissim, Malvina
%Y Berant, Jonathan
%Y Lenci, Alessandro
%S Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F cui-etal-2018-solving
%X 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.
%R 10.18653/v1/S18-2030
%U https://aclanthology.org/S18-2030
%U https://doi.org/10.18653/v1/S18-2030
%P 255-264

Markdown (Informal)

[Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition](https://aclanthology.org/S18-2030) (Cui et al., *SEM 2018)

• Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition (Cui et al., *SEM 2018)

ACL

• Xia Cui, Sadamori Kojaku, Naoki Masuda, and Danushka Bollegala. 2018. Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 255–264, New Orleans, Louisiana. Association for Computational Linguistics.