國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Prediction of Outcomes in Higher Courts of Turkey Using Natural Language Processing =/
其他題名:	Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
其他題名:	Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini.
作者:	Mumcuoglu, Emre.
面頁冊數:	1 online resource (71 pages)
附註:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
標題:	Political science. -
電子資源:	click for full text (PQDT)
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.Dogal Dil Isleme Yontemleri Kullanilarak Turk Yuksek Mahkemelerinde Karar Tahmini. - 1 online resource (71 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798841571407Subjects--Topical Terms:

558774
Political science.
Index Terms--Genre/Form:

554714
Electronic books.

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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502 $a Thesis (M.Sc.)--Bilkent Universitesi (Turkey), 2022.
504 $a Includes bibliographical references
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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