February 10, 2022 Conference paper Open Access

Pantelis Kyriakidis; Despoina Chatzakou; Theodora Tsikrika; Stefanos Vrochidis; Ioannis Kompatsiaris

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  <identifier identifierType="DOI">10.5281/zenodo.6036739</identifier>
  <creators>
    <creator>
      <creatorName>Pantelis Kyriakidis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5963-8776</nameIdentifier>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Despoina Chatzakou</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9564-7100</nameIdentifier>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Theodora Tsikrika</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4148-9028</nameIdentifier>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Stefanos Vrochidis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2505-9178</nameIdentifier>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
    <creator>
      <creatorName>Ioannis Kompatsiaris</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6447-9020</nameIdentifier>
      <affiliation>Information Technologies Institute, Centre for Research and Technology Hellas</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Leveraging Transformer Self Attention Encoder for Crisis Event Detection in Short Texts</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Self attention</subject>
    <subject>Multihead attention</subject>
    <subject>Crisis event detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-02-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6036739</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6036738</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/aidaproject</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/2.0/legalcode">Creative Commons Attribution 2.0 Generic</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Analyzing content generated on social media has proven to be a powerful tool for early detection of crisis-related events. Such an analysis may allow for timely action, mitigating or even preventing altogether the effects of a crisis. However, the high noise levels in short texts present in microblogging platforms, combined with the limited publicly available datasets have rendered the task difficult. Here, we propose deep learning models based on a transformer self-attention encoder, which is capable of detecting event-related parts in a text, while also minimizing potential noise levels. Our models efficacy is shown by experimenting with CrisisLexT26, achieving up to 81.6% f1-score and 92.7% AUC.&lt;/p&gt;</description>
    <description descriptionType="Other">This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in the 44th European Conference on Information Retrieval, and is available online at https://doi.org/10.1007/978-3-030-99739-7_19</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/883293/">883293</awardNumber>
      <awardTitle>IMMERSE. INTERACT. INVESTIGATE</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/883596/">883596</awardNumber>
      <awardTitle>Artificial Intelligence and advanced Data Analytics for Law Enforcement Agencies</awardTitle>
    </fundingReference>
  </fundingReferences>
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