Conference paper Open Access

Effective Unsupervised Author Disambiguation with Relative Frequencies

Backes, Tobias

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      <creatorName>Backes, Tobias</creatorName>
      <affiliation>GESIS - Leibniz-Institute for the Social Sciences</affiliation>
    <title>Effective Unsupervised Author Disambiguation with Relative Frequencies</title>
    <subject>Author Disambiguation</subject>
    <subject>Agglomerative Clustering</subject>
    <date dateType="Issued">2018-06-03</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3197026.3197036</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This work addresses the problem of author name homonymy in the Web of Science. Aiming for an efficient, simple and straightforward solution, we introduce a novel probabilistic similarity measure for author name disambiguation based on feature overlap. Using the researcher-ID available for a subset of the Web of Science, we evaluate the application of this measure in the context of agglomeratively clustering author mentions. We focus on a concise evaluation that shows clearly for which problem setups and at which time during the clustering process our approach works best. In contrast to most&lt;br&gt;
other works in this field, we are skeptical towards the performance of author name disambiguation methods in general and compare our approach to the trivial single-cluster baseline. Our results are presented separately for each correct clustering size as we can explain that, when treating all cases together, the trivial baseline and more sophisticated approaches are hardly distinguishable in terms of evaluation results. Our model shows state-of-the-art performance for all correct&amp;nbsp; clustering sizes without any discriminative training and with tuning only one convergence parameter.&lt;/p&gt;</description>
      <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>
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