April 12, 2021 Conference paper Open Access

Boroş, Emanuela; Doucet, Antoine

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  <identifier identifierType="DOI">10.5281/zenodo.4681008</identifier>
  <creators>
    <creator>
      <creatorName>Boroş, Emanuela</creatorName>
      <givenName>Emanuela</givenName>
      <familyName>Boroş</familyName>
      <affiliation>University of La Rochelle, L3i, F-17000, La Rochelle, France</affiliation>
    </creator>
    <creator>
      <creatorName>Doucet, Antoine</creatorName>
      <givenName>Antoine</givenName>
      <familyName>Doucet</familyName>
      <affiliation>University of La Rochelle, L3i, F-17000, La Rochelle, France</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-04-12</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4681008</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4681007</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/embeddia</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;p&gt;This paper summarizes the participation of the Laboratoire Informatique, Image et Interaction (L3i laboratory) of the University of La Rochelle in the Recognizing Ultra Finegrained Entities (RUFES) track1 within the Text Analysis Conference (TAC) series of evaluation workshops. Our participation relies on two neural-based models, one based on a pretrained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box model for within-document entity coreference. We observe that our approach has great potential in increasing the performance of fine-grained entity recognition. Thus, the future work envisioned is to enhance the ability of the models following additional experiments and a deeper analysis of the results.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825153/">825153</awardNumber>
      <awardTitle>Cross-Lingual Embeddings for Less-Represented Languages in European News Media</awardTitle>
    </fundingReference>
  </fundingReferences>
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