1 Ocak 2023 Dergi makalesi Açık Erişim

Cetindag, Can; Yazicioglu, Berkay; Koc, Aykut

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/254559</identifier>
  <creators>
    <creator>
      <creatorName>Cetindag, Can</creatorName>
      <givenName>Can</givenName>
      <familyName>Cetindag</familyName>
    </creator>
    <creator>
      <creatorName>Yazicioglu, Berkay</creatorName>
      <givenName>Berkay</givenName>
      <familyName>Yazicioglu</familyName>
    </creator>
    <creator>
      <creatorName>Koc, Aykut</creatorName>
      <givenName>Aykut</givenName>
      <familyName>Koc</familyName>
    </creator>
  </creators>
  <titles>
    <title>Named-Entity Recognition In Turkish Legal Texts</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/254559</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1017/S1351324922000304</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">Natural language processing (NLP) technologies and applications in legal text processing are gaining momentum. Being one of the most prominent tasks in NLP, named-entity recognition (NER) can substantiate a great convenience for NLP in law due to the variety of named entities in the legal domain and their accentuated importance in legal documents. However, domain-specific NER models in the legal domain are not well studied. We present a NER model for Turkish legal texts with a custom-made corpus as well as several NER architectures based on conditional random fields and bidirectional long-short-term memories (BiLSTMs) to address the task. We also study several combinations of different word embeddings consisting of GloVe, Morph2Vec, and neural network-based character feature extraction techniques either with BiLSTM or convolutional neural networks. We report 92.27% F1 score with a hybrid word representation of GloVe and Morph2Vec with character-level features extracted with BiLSTM. Being an agglutinative language, the morphological structure of Turkish is also considered. To the best of our knowledge, our work is the first legal domain-specific NER study in Turkish and also the first study for an agglutinative language in the legal domain. Thus, our work can also have implications beyond the Turkish language.</description>
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