Conference paper Open Access

StoryLens: A Multiple Views Corpus for Location and Event Detection

Braşoveanu, Adrian M. P.; Nixon, Lyndon J. B.; Weichselbraun, Albert

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      <creatorName>Braşoveanu, Adrian M. P.</creatorName>
      <givenName>Adrian M. P.</givenName>
      <affiliation>Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</affiliation>
      <creatorName>Nixon, Lyndon J. B.</creatorName>
      <givenName>Lyndon J. B.</givenName>
      <affiliation>MODUL Technology GmbH Vienna, Austria</affiliation>
      <creatorName>Weichselbraun, Albert</creatorName>
      <affiliation>Swiss Institute for Information Research - University of Applied Sciences Chur Chur, Switzerland</affiliation>
    <title>StoryLens: A Multiple Views Corpus for Location and Event Detection</title>
    <subject>Named Entity Linking</subject>
    <subject>Event detection</subject>
    <subject>Information Extraction</subject>
    <subject>Natural Language Processing</subject>
    <subject>Fake news</subject>
    <date dateType="Issued">2018-06-27</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3227609.3227674</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;The news media landscape tends to focus on long-running narratives. Correctly processing new information, therefore, requires considering multiple lenses when analyzing media content. Traditionally it would have been considered sufficient to extract the topics&amp;nbsp;or entities contained in a text in order to classify it, but today it is important to also look at more sophisticated annotations related to fine grained geolocation, events, stories and the relations between them. In order to leverage such lenses we propose a new corpus that offers a diverse set of annotations over texts collected from multiple media sources. We also showcase the framework used for creating the corpus, as well as how the information from the various lenses can be used in order to support different use cases in the EU project InVID for verifying the veracity of online video.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687786/">687786</awardNumber>
      <awardTitle>In Video Veritas – Verification of Social Media Video Content for the News Industry</awardTitle>
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