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  <doc>
    <id>1789</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>70</pageFirst>
    <pageLast>78</pageLast>
    <pageNumber>9</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Association for Computational Linguistics</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-11-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning</title>
    <abstract language="eng">Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification models suffer from event-related biases and highly imbalanced label distributions which still poses a challenging task. To address these challenges, we propose a combination of entity-masked language modeling and hierarchical multi-label classification as a multi-task learning problem. We evaluate our method on tweets from the TREC-IS dataset and show an absolute performance gain w.r.t. F1-score of up to 10% for actionable information types. Moreover, we found that entity-masking reduces the effect of overfitting to in-domain events and enables improvements in cross-event generalization.</abstract>
    <identifier type="doi">10.48550/arXiv.2211.11468</identifier>
    <identifier type="arxiv">arXiv:2211.11468</identifier>
    <enrichment key="ConferenceStatement">2nd Workshop on NLP for Positive Impact</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.import.date">2024-06-27T13:39:45+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Deutsches Urheberrecht gilt</licence>
    <author>Philipp Seeberger</author>
    <author>Korbinian Riedhammer</author>
    <collection role="institutes" number="">Fakultät Informatik</collection>
  </doc>
</export-example>
