Conference paper Open Access

Performance Comparison of Ad-hoc Retrieval Models over Full-text vs. Titles of Documents

Saleh, Ahmed; Beck, Tilman; Galke, Lukas; Scherp, Ansgar


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  <identifier identifierType="URL">https://zenodo.org/record/2547476</identifier>
  <creators>
    <creator>
      <creatorName>Saleh, Ahmed</creatorName>
      <givenName>Ahmed</givenName>
      <familyName>Saleh</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Beck, Tilman</creatorName>
      <givenName>Tilman</givenName>
      <familyName>Beck</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Galke, Lukas</creatorName>
      <givenName>Lukas</givenName>
      <familyName>Galke</familyName>
      <affiliation>ZBW - Leibniz Information Centre for Economics</affiliation>
    </creator>
    <creator>
      <creatorName>Scherp, Ansgar</creatorName>
      <givenName>Ansgar</givenName>
      <familyName>Scherp</familyName>
      <affiliation>University of Stirling</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Performance Comparison of Ad-hoc Retrieval Models over Full-text vs. Titles of Documents</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Information Retrieval</subject>
    <subject>Learning to Rank</subject>
    <subject>Deep Learning</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-11-15</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2547476</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-030-04257-8_30</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <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;pre&gt;While there are many studies on information retrieval models using full-text, there are presently no comparison studies of full-text retrieval vs. retrieval only over the titles of documents. On the one hand, the full-text of documents like scientific papers is not always available due to, e.,g., copyright policies of academic publishers. &lt;/pre&gt;

&lt;pre&gt;On the other hand, conducting a search based on titles alone has strong limitations. Titles are short and therefore may not contain enough information to yield satisfactory search results. In this paper, we compare different retrieval models regarding their search performance on the full-text vs. only titles of documents. &lt;/pre&gt;

&lt;pre&gt;We use different datasets, including the three digital library datasets:  EconBiz, IREON, and PubMed. The results show that it is possible to build effective title-based retrieval models that provide competitive results comparable to full-text retrieval. The difference between the average evaluation results of the best title-based retrieval models is only 3% less than those of the best full-text-based retrieval models. &lt;/pre&gt;

&lt;pre&gt;&amp;nbsp;&lt;/pre&gt;</description>
    <description descriptionType="Other">This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in the proceedings of the International Conference on Asian Digital Libraries ICADL 2018,   https://doi.org/10.1007/978-3-030-04257-8_30.</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/693092/">693092</awardNumber>
      <awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
    </fundingReference>
  </fundingReferences>
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