1 Ocak 2023 Dergi makalesi Açık Erişim

Cetindag, Can; Yazicioglu, Berkay; Koc, Aykut

Dublin Core

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  <dc:creator>Cetindag, Can</dc:creator>
  <dc:creator>Yazicioglu, Berkay</dc:creator>
  <dc:creator>Koc, Aykut</dc:creator>
  <dc:date>2023-01-01</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/254559</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:254559</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>NATURAL LANGUAGE ENGINEERING 29(3) 615-642</dc:source>
  <dc:title>Named-entity recognition in Turkish legal texts</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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