Türkiye'de hukuki metin sınıflandırması: Boşanma ve imar kararlarına makine öğrenmesi yaklaşımı

Yıl 2024, Cilt: 6 Sayı: 2, 53 - 63, 20.12.2024

Tülay Turan , Ecir Uğur Küçüksille

https://doi.org/10.70669/ijedt.1491511

Öz

Son yıllarda artan hukuki veri hacmi, verimli yönetim ve kullanım için Yapay Zeka (AI) tekniklerinin entegrasyonunu gerektirmektedir. Kritik zorluklar arasında hukuki metinlerin belirli alanlara veya konulara göre sınıflandırılması yer almaktadır; bu, hukuki araştırma ve uygulamanın ilerletilmesi açısından çok önemlidir. Bu makale, uluslararası hukuk metinlerindeki sınıflandırma çalışmalarına kıyasla daha önce yeterince araştırılmamış bir alan olan Türk mahkeme kararlarını kategorik olarak sınıflandırmayı amaçlamaktadır. Çalışma, hukuki metinleri belirli alanlara ayırarak, Türk hukuki kararlarında yönlendirmeye yönelik yapay zeka destekli çözümlerin geliştirilmesine önemli ölçüde katkıda bulunmayı ve böylece hukuk sisteminin verimliliğini ve erişilebilirliğini artırmayı amaçlamaktadır. Çalışmada ilk olarak boşanma ve imar davalarından oluşan bir veri seti oluşturulmuştur. Daha sonra davaları en etkin şekilde sınıflandıracak algoritmayı belirlemek için K-En Yakın Komşu (KNN), Destek Vektör Makineleri (SVM), Karar Ağaçları (DT) ve Rastgele Ormanlar (RF) algoritmaları ile temel modeller kurulmuştur. Temel Model performanslarını arttırmak için her bir model için hiperparametre optimizasyonu gerçekleştirilmiştir. Bu süreç, 10 katlı çapraz doğrulama yöntemi ile desteklenmiştir. Optimizasyon sonucunda elde edilen hiperparametre değerleri ile iyileştirilmiş modeller kurulmuştur. Karşılaştırmalı analiz sonucunda, SVM modeli hukuki metinlerin sınıflandırılmasında %90 gibi etkileyici bir doğruluk oranına sahip olmuştur. Bu sonuç, Türkiye'deki hukuki metinlerin sınıflandırılmasında önemli bir başarıyı elde ederek, akıllı hukuk sistemlerinin gelişimine önemli katkılar sağlayacaktır.

Anahtar Kelimeler

Hukuki Metin Sınıflandırması, Türk Mahkeme Kararları, Makine Öğrenmesi Algoritmaları, Hiperparametre Optimizasyonu, SVM

Legal text classification in Turkey: A machine learning approach to divorce and zoning decisions

Öz

The increasing volume of legal data in recent years requires integrating artificial intelligence (AI) techniques for efficient management and use. Critical challenges include classifying legal texts into specific fields or topics. This is crucial to advancing legal research and practice. This article aims to categorically classify Turkish court decisions, an area that has yet to be adequately researched before, compared to classification studies in international law texts. The study aims to contribute significantly to developing artificial intelligence-supported solutions to guide Turkish legal decisions by dividing legal texts into specific areas, thus increasing the efficiency and accessibility of the legal system. The study first created a data set consisting of divorce and zoning cases. Then, basic models were established with K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF) algorithms to determine the algorithm that would classify the cases most effectively. Hyperparameter optimization was performed for each model to increase the Base Model performances. This process was supported by the 10-fold cross-validation method. Improved models were established with the hyperparameter values obtained as a result of optimization. As a result of comparative analysis, the SVM model had an impressive 90% accuracy rate in classifying legal texts. This result will significantly contribute to the development of intelligent legal systems by achieving significant success in classifying legal texts in Turkey.

Anahtar Kelimeler

Legal Text Classification, Turkish Court Decisions, Machine Learning Algorithms, Hyperparameter Optimization, SVM

Kaynakça

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