@inproceedings{87e1d7870f6a48f8bb1797a18e108b73,
title = "Legal entity recognition in indonesian court decision documents using Bi-LSTM and CRF approaches",
abstract = "The increasing number of Indonesian court decision documents requires special attention to be managed and used efficiently. One thing is to recognize legal entities to identify relevant legal information in the documents. This research examines the effectiveness of several deep learning methods, to recognize ten legal entities in Indonesian court decision documents. In this task, we found that the combination of Bi-LSTM and CRF methods achieve the highest F-1 score of 0.83. It outperforms other deep learning methods (CNN, LSTM, LSTM+CRF and Bi-LSTM) by 2 - 12 % and machine learning methods (SVM and CRF) by 41 - 76 %. The results show that this model can be used to identify relevant information about the legal domain in Indonesia.",
keywords = "Bi-lstm, CRF, Legal document, Legal entity recognition, Legal processing, Lstm, Name entity recognition, ner",
author = "Nuranti, {Eka Qadri} and Evi Yulianti",
note = "Funding Information: This research is supported by the PUTI (Publikasi Terindeks International) Prosiding grant in 202 from Universitias Indonesia). Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020 ; Conference date: 17-10-2020 Through 18-10-2020",
year = "2020",
month = oct,
day = "17",
doi = "10.1109/ICACSIS51025.2020.9263157",
language = "English",
series = "2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "429--434",
booktitle = "2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020",
address = "United States",
}