Journal article Open Access

Short‐text feature expansion and classification based on nonnegative matrix factorization

Zhang, Ling; Jiang, Wenchao; Zhao, Zhiming


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  <identifier identifierType="URL">https://zenodo.org/record/4042991</identifier>
  <creators>
    <creator>
      <creatorName>Zhang, Ling</creatorName>
      <givenName>Ling</givenName>
      <familyName>Zhang</familyName>
      <affiliation>Guangdong University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Jiang, Wenchao</creatorName>
      <givenName>Wenchao</givenName>
      <familyName>Jiang</familyName>
      <affiliation>Guangdong University of Technology</affiliation>
    </creator>
    <creator>
      <creatorName>Zhao, Zhiming</creatorName>
      <givenName>Zhiming</givenName>
      <familyName>Zhao</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6717-9418</nameIdentifier>
      <affiliation>University of Amsterdam</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Short‐text feature expansion and classification based on nonnegative matrix factorization</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>correlation</subject>
    <subject>feature extension</subject>
    <subject>nonnegative matrix factorization</subject>
    <subject>short text classification</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-09-22</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4042991</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/int.22290</relatedIdentifier>
  </relatedIdentifiers>
  <version>camera ready</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;In this paper, a non‐negative matrix factorization feature&lt;/p&gt;

&lt;p&gt;expansion (NMFFE) approach was proposed to&lt;/p&gt;

&lt;p&gt;overcome the feature‐sparsity issue when expanding&lt;/p&gt;

&lt;p&gt;features of short‐text. First, we took the internal relationships&lt;/p&gt;

&lt;p&gt;of short texts and words into account when&lt;/p&gt;

&lt;p&gt;segmenting words from texts and constructing their&lt;/p&gt;

&lt;p&gt;relationship matrix. Second, we utilized the Dual&lt;/p&gt;

&lt;p&gt;regularization non‐negative matrix tri‐factorization&lt;/p&gt;

&lt;p&gt;(DNMTF) algorithm to obtain the words clustering&lt;/p&gt;

&lt;p&gt;indicator matrix, which was used to get the feature&lt;/p&gt;

&lt;p&gt;space by dimensionality reduction methods. Thirdly,&lt;/p&gt;

&lt;p&gt;words with close relationship were selected out from&lt;/p&gt;

&lt;p&gt;the feature space and added into the short‐text to solve&lt;/p&gt;

&lt;p&gt;the sparsity issue. The experimental results showed&lt;/p&gt;

&lt;p&gt;that the accuracy of short text classification of our&lt;/p&gt;

&lt;p&gt;NMFFE algorithm increased 25.77%, 10.89%, and 1.79%&lt;/p&gt;

&lt;p&gt;on three data sets: Web snippets, Twitter sports, and&lt;/p&gt;

&lt;p&gt;AGnews, respectively compared with the Word2Vec&lt;/p&gt;

&lt;p&gt;algorithm and Char‐CNN algorithm. It indicated that&lt;/p&gt;

&lt;p&gt;the NMFFE algorithm was better than the BOW algorithm&lt;/p&gt;

&lt;p&gt;and the Char‐CNN algorithm in terms of classification&lt;/p&gt;

&lt;p&gt;accuracy and algorithm robustness.&lt;/p&gt;</description>
  </descriptions>
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    <fundingReference>
      <funderName>European Commission</funderName>
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      <funderName>European Commission</funderName>
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