Natural language processing in law: Prediction of outcomes in the higher courts of Turkey

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buir.contributor.authorMumcuoğlu, Emre
buir.contributor.authorÖztürk, Ceyhun E.
buir.contributor.authorÖzaktaş, Haldun Memduh
buir.contributor.authorKoç, Aykut
buir.contributor.orcidMumcuoğlu, Emre|0000-0002-1201-1941
buir.contributor.orcidÖztürk, Ceyhun E.|0000-0001-9744-6778
dc.citation.epage102684-16en_US
dc.citation.spage102684-1en_US
dc.citation.volumeNumber58en_US
dc.contributor.authorMumcuoğlu, Emre
dc.contributor.authorÖztürk, Ceyhun E.
dc.contributor.authorÖzaktaş, Haldun Memduh
dc.contributor.authorKoç, Aykut
dc.date.accessioned2022-02-15T06:17:04Z
dc.date.available2022-02-15T06:17:04Z
dc.date.issued2021-09
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractNatural 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.en_US
dc.embargo.release2023-09-30
dc.identifier.doi10.1016/j.ipm.2021.102684en_US
dc.identifier.issn0306-4573
dc.identifier.urihttp://hdl.handle.net/11693/77342
dc.language.isoEnglishen_US
dc.publisherElsevier Ltden_US
dc.relation.isversionofhttps://doi.org/10.1016/j.ipm.2021.102684en_US
dc.source.titleInformation Processing & Managementen_US
dc.subjectNatural language processingen_US
dc.subjectLawen_US
dc.subjectMachine learningen_US
dc.subjectAI in lawen_US
dc.subjectLegal text miningen_US
dc.subjectDeep learningen_US
dc.titleNatural language processing in law: Prediction of outcomes in the higher courts of Turkeyen_US
dc.typeArticleen_US

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Department of Electrical and Electronics Engineering
National Magnetic Resonance Research Center (UMRAM)