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Prediction of Outcomes in Higher Cou...

Mumcuoglu, Emre.

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Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing = = Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing =/
Reminder of title:	Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
Author:	Mumcuoglu, Emre.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	71 p.
Notes:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
Subject:	Language. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29274669
ISBN:	9798841571407

Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing = = Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
Mumcuoglu, Emre.

Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing =Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 71 p.

Source: Masters Abstracts International, Volume: 84-02.

Thesis (M.Sc.)--Bilkent Universitesi (Turkey), 2022.

This item must not be sold to any third party vendors.

The use of Natural Language Processing (NLP) in the field of law has become a topic of interest in the recent years. Applications to Turkish law, however, have remained unexplored to this day. In this thesis, first, a review of existing NLP applications in law is provided, and then, the problem of predicting Turkish court decisions is studied using NLP techniques. An extensive corpus that consists of case texts from Turkish higher courts, namely, the Constitutional Court and District Courts, is compiled. In addition, a numerical analysis and comparison of NLP methods at predicting the outcomes of these higher court cases is provided. The methods used for prediction are based on Decision Trees, Random Forests, Support Vector Machines and various deep learning models; specifically Gated Recurrent Units, unidirectional and bidirectional Long Short-Term Memory networks, and their attention-integrated counterparts. Prediction results for all algorithms are presented comparatively across all courts. The results show that decisions of Turkish higher courts can be predicted with high accuracy, especially with deep learning-based methods. Similar performance to existing work in the literature on case outcome prediction, which focus on different languages and different legal systems, is achieved.

ISBN: 9798841571407Subjects--Topical Terms:

643551
Language.

Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing = = Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
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245 1 0 $a Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing = $b Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
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500 $a Source: Masters Abstracts International, Volume: 84-02.
500 $a Advisor: Ozaktas, Memduh Haldun;Koc, Aykut;Cukur, Tolga;Turkmen, Arzucan Ozgur.
502 $a Thesis (M.Sc.)--Bilkent Universitesi (Turkey), 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The use of Natural Language Processing (NLP) in the field of law has become a topic of interest in the recent years. Applications to Turkish law, however, have remained unexplored to this day. In this thesis, first, a review of existing NLP applications in law is provided, and then, the problem of predicting Turkish court decisions is studied using NLP techniques. An extensive corpus that consists of case texts from Turkish higher courts, namely, the Constitutional Court and District Courts, is compiled. In addition, a numerical analysis and comparison of NLP methods at predicting the outcomes of these higher court cases is provided. The methods used for prediction are based on Decision Trees, Random Forests, Support Vector Machines and various deep learning models; specifically Gated Recurrent Units, unidirectional and bidirectional Long Short-Term Memory networks, and their attention-integrated counterparts. Prediction results for all algorithms are presented comparatively across all courts. The results show that decisions of Turkish higher courts can be predicted with high accuracy, especially with deep learning-based methods. Similar performance to existing work in the literature on case outcome prediction, which focus on different languages and different legal systems, is achieved.
520 $a Dogal Dil Isleme'nin (DDI) hukuk alanindaki uygulamalari arastirmacilarin son yillarda artarak ilgisini cekerken Turk hukuku icin olasi uygulamalari inceleyen bir calisma bulunmamaktadir. Biz, ilk olarak DDI'nin halihazirda literaturde olan hukuki uygulamalarini gozden geciriyor, sonra da Turk mahkemeleri ozelinde dava metinlerinden mahkeme kararini tahmin etme problemini ele aliyoruz. Bu tezin alana baslica iki katkisindan ilki, Turk yuksek mahkemelerinden Anayasa Mahkemesi ve Istinaf Mahkemelerinin karar metinlerinden kapsamli bir der- lem olusturulmasidir. Ikinci katkisi ise, bu dava metinleri uzerinden mahke- menin kararini tahmin etmede cesitli DDI yontemlerinin nicel olarak incelen- mesi ve kiyaslanmasidir. Karar tahmin etmede kullanilan algoritmalar Karar Agaclari, Rastgele Ormanlar, Destek Vektoru Makineleri ve muhtelif derin ogrenme yontemlerine dayanmaktadir. Sozu gecen derin ogrenme yontemleri Gecitli Mukerrer Hucreler, tek yonlu ve cift yonlu Uzun-Kisa Vade Hafiza aglari ve bunlarin dikkat mekanizmasi eklenmis cesitleridir. Butun bu algoritmalarin karar tahminindeki performanslarmi her bir mahkeme icin mukayeseli bir bicimde ortaya koyuyoruz. Turk yuksek mahkemelerinin kararlarinin ozellikle derin ogrenme yontemleri kullanilarak yuksek isabet oranlariyla tahmin edilebilecegini gosteriyoruz. Diger ulkelerin mahkemeleri icin farkli dillerdeki dava metinlerinde yapilmis benzer calismalara yakin sonuclar elde ediyoruz.
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