1 Ocak 2023 Dergi makalesi Açık Erişim

Cetindag, Can; Yazicioglu, Berkay; Koc, Aykut

Citation Style Language JSON

{
  "DOI": "10.1017/S1351324922000304", 
  "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.", 
  "author": [
    {
      "family": "Cetindag", 
      "given": " Can"
    }, 
    {
      "family": "Yazicioglu", 
      "given": " Berkay"
    }, 
    {
      "family": "Koc", 
      "given": " Aykut"
    }
  ], 
  "container_title": "NATURAL LANGUAGE ENGINEERING", 
  "id": "254559", 
  "issue": "3", 
  "issued": {
    "date-parts": [
      [
        2023, 
        1, 
        1
      ]
    ]
  }, 
  "page": "615-642", 
  "title": "Named-entity recognition in Turkish legal texts", 
  "type": "article-journal", 
  "volume": "29"
}