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      Predicting outcomes of the court of cassation of Turkey with recurrent neural networks

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      Author(s)
      Öztürk, Ceyhun E.
      Özçelik, Ş. Barış
      Koç, Aykut
      Date
      2022-08-29
      Source Title
      Signal Processing and Communications Applications Conference (SIU)
      Print ISSN
      2165-0608
      Publisher
      IEEE
      Pages
      [1] - [4]
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      Natural Language Processing (NLP) based approaches have recently become very popular for studies in legal domain. In this work, the outcomes of the cases of the Court of Cassation of Turkey were predicted with the use of Deep Learning models. These models are GRU, LSTM and BiLSTM which are variants of the recurrent neural network. Models saw only fact description parts of the case decision texts during training. Firstly, the models were trained with the word embeddings that were created from the texts from daily language. Then, the models were trained with the word embeddings that were created from downloaded legal cases from Turkish courts. The results of the experiments on the models are given in a comparative and detailed manner. It is observed based on this study and the past studies that the outcomes of the Court of Cassation can be predicted with higher accuracy than most of the courts that were investigated in previous legal NLP studies. The model which is best at predicting decisions is GRU. The GRU model achieves 96.8% accuracy in the decision prediction task.
       
      Doğal dil işleme (NLP) tabanlı yaklaşımlar hukuk çalışmalarında son dönemde çok popüler hâle gelmiştir. Bu çalışmada Yargıtay davalarının sonuçları derin öğrenme modelleri kullanılarak tahmin edilmiştir. Bu modeller mükerrer sinir ağı türevi olan GRU, LSTM ve BiLSTM’dir. Modeller eğitim sırasında karar metinlerinin sadece olay tanımları olan kısımlarını görmüştür. İlk olarak modeller günlük dilden metinlerle üretilen kelime temsilleriyle egitilmiştir. Daha sonra modeller Türk mahkemelerinden indirilen davalarla üretilen kelime temsilleriyle eğitilmiştir. Modeller üzerinde yapılan deneylerin sonuçları karşılaştırmalı ve detaylı biçimde verilmiştir. Bu çalışma ve önceki çalışmalara bakılarak Yargıtay kararlarının hukukta yürütülen NLP çalışmalarında incelenen çoğu mahkemeden daha yüksek isabetle tahmin edilebildiği görülmektedir. En başarılı karar tahmini yapan model GRU olarak gözlenmiştir. GRU modeli ile karar tahmininde %96.8 doğruluk skoruna ulaşılmıştır.
      Keywords
      Natural language processing
      Law
      Machine learning
      AI in law
      Legal text mining
      Deep learning
      Doğal dil işleme
      Hukuk
      Makine öğrenmesi
      Hukukta yapay zeka
      Hukuki metin işleme
      Derin öğrenme
      Permalink
      http://hdl.handle.net/11693/111316
      Published Version (Please cite this version)
      https://www.doi.org/10.1109/SIU55565.2022.9864914
      Collections
      • Department of Electrical and Electronics Engineering 4011
      • Department of Law 269
      • National Magnetic Resonance Research Center (UMRAM) 301
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