Öztürk, Ceyhun Emre2023-07-142023-07-142023-062023-062023-07-11https://hdl.handle.net/11693/112417Cataloged from PDF version of article.Includes bibliographical references (leaves 32-41).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.xi, 41 leaves : color illustrations, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessLawDeep learningInformation retrievalNLP in lawAI in lawPrior legal case retrievalNatural language processingThesisB162220