Retrieving Turkish prior legal cases with deep learning

Date

2023-06

Editor(s)

Advisor

Koç, Aykut

Supervisor

Co-Advisor

Co-Supervisor

Instructor

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Abstract

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.

Source Title

Publisher

Course

Other identifiers

Book Title

Degree Discipline

Electrical and Electronic Engineering

Degree Level

Master's

Degree Name

MS (Master of Science)

Citation

Published Version (Please cite this version)

Language

English

Type