A Transformer-based Prior Legal Case Retrieval Method

被引：0
|
作者：
Ozturk, Ceyhun E. [1 ]
Ozcelik, S. Baris [2 ]
Koc, Aykut [3 ]
机构：
[1] Bilkent Univ, Elekt & Elekt Muhendisligi Bolumu, ASELSAN Res Ctr, Ankara, Turkiye
[2] Bilkent Univ, Hukuk Fak, Ankara, Turkiye
[3] Bilkent Univ, Elekt & Elekt Muhendisligi Bolumu, Ulusal Manyet Rezonans Arastirma Merkezi, Ankara, Turkiye
来源：
2023 31ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU | 2023年
关键词：
Natural language processing; legal tech; deep learning; prior legal case retrieval; legal NLP; Turkish NLP;
D O I：
10.1109/SIU59756.2023.10223938
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work, BERTurk-Legal, a transformer-based language model, is introduced to retrieve prior legal cases. BERTurk-Legal is pre-trained on a dataset from the Turkish legal domain. This dataset does not contain any labels related to the prior court case retrieval task. Masked language modeling is used to train BERTurk-Legal in a self-supervised manner. With zero-shot classification, BERTurk-Legal provides state-of-the-art results on the dataset consisting of legal cases of the Court of Cassation of Turkey. The results of the experiments show the necessity of developing language models specific to the Turkish law domain.
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页数：4
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