Browsing by Subject "AI in law"

Now showing 1 - 3 of 3

Open Access
Natural language processing in law: Prediction of outcomes in the higher courts of Turkey
(Elsevier Ltd, 2021-09) Mumcuoğlu, Emre; Öztürk, Ceyhun E.; Özaktaş, Haldun Memduh; Koç, Aykut
Natural language processing (NLP) based approaches have recently received attention for legal systems of several countries. It is of interest to study the wide variety of legal systems that have so far not received any attention. In particular, for the legal system of the Republic of Turkey, codified in Turkish, no works have been published. We first review the state-of-the-art of NLP in law, and then study the problem of predicting verdicts for several different courts, using several different algorithms. This study is much broader than earlier studies in the number of different courts and the variety of algorithms it includes. Therefore it provides a reference point and baseline for further studies in this area. We further hope the scope and systematic nature of this study can set a framework that can be applied to the study of other legal systems. We present novel results on predicting the rulings of the Turkish Constitutional Court and Courts of Appeal, using only fact descriptions, and without seeing the actual rulings. The methods that are utilized are based on Decision Trees (DTs), Random Forests (RFs), Support Vector Machines (SVMs) and state-of-the-art deep learning (DL) methods; specifically Gated Recurrent Units (GRUs), Long Short-Term Memory networks (LSTMs) and bidirectional LSTMs (BiLSTMs), with the integration of an attention mechanism for each model. The prediction results for all algorithms are given in a comparative and detailed manner. We demonstrate that outcomes of the courts of Turkish legal system can be predicted with high accuracy, especially with deep learning based methods. The presented results exhibit similar performance to earlier work in the literature for other languages and legal systems.
Open Access
Predicting outcomes of the court of cassation of Turkey with recurrent neural networks
(IEEE, 2022-08-29) Öztürk, Ceyhun E.; Özçelik, Ş. Barış; Koç, Aykut
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.
Open Access
Retrieving Turkish prior legal cases with deep learning
(2023-06) Öztürk, Ceyhun Emre
This study utilizes deep learning models to retrieve prior legal cases in the Court of Cassation in Turkey. Given the vast legal databases that legal professionals need to navigate and the ability of computers to handle large amounts of text quickly, information retrieval algorithms prove beneficial for legal practitioners. In this thesis, we introduce our legal recurrent neural network (RNN) models and the BERTurk-Legal model. We also introduce dense word embeddings for the Turkish legal domain. Moreover, we employ RNN autoencoders, Legal RNN autoencoders, combinations of RNN autoencoders with BM25 algorithms, and BERTurk-Legal to retrieve prior legal cases. We obtain the best results with the BERTurk-Legal model.