Legal Entity Recognition in Indonesian Court Decision Documents Using Bi-LSTM and CRF Approaches

Nuranti, Eka Qadri; Yulianti, Evi

doi:10.1109/icacsis51025.2020.9263157

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…As of June 2, 2023, the Indonesian Supreme Court alone has issued over 7.8 million court decisions. One study [15] employed deep learning and NLP techniques to retrieve court decisions and extract legal entities mentioned within them. The annotation process involved identifying and categorizing ten specific entities, including individuals involved in the legal proceedings (e.g., advocates, judges, prosecutors) as well as various document-related entities (e.g., laws, decision numbers, punishments).…”

Section: Legal Research In Indonesiamentioning

confidence: 99%

A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs

Faisal,

Darari,

Ghifari

et al. 2023

Jurnal Ilmu Komputer dan Informasi

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Virtual assistants have gained popularity across various domains, including the legal field, where they serve to offer guidance and aid in the form of law retrieval. In this research, our aim is to develop a legal virtual assistant that combines knowledge graphs (KGs) and information retrieval (IR) techniques. This hybrid approach allows us to provide accurate answers extracted from structured interconnected data while simultaneously cater to a diverse range of legal inquiries. We categorize these inquiries into a few distinct use cases: definition lookup, law component lookup, sanctions, and domain knowledge. Our system encompasses a chatbot platform, knowledge graph querying, and information retrieval. Specifically, we construct a VA system over a legal knowledge graph pertaining to the Indonesian Act concerning Manpower or Labor (UU Ketenagakerjaan) and the Indonesian Act concerning the Creation of Jobs (UU Cipta Kerja). This marks the creation of the first legal virtual assistant in the Indonesian context that combines KG and IR methodologies. To evaluate the effectiveness of our prototype system, we conduct tests using a variety of labor law-related questions, ranging in difficulty. The integration of knowledge graphs and information retrieval proves to significantly improve the support provided for a wide range of potential applications in the legal field.

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Section: Legal Research In Indonesiamentioning

confidence: 99%

A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs

Faisal,

Darari,

Ghifari

et al. 2023

Jurnal Ilmu Komputer dan Informasi

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“…Beberapa penelitian mengimplementasikan NER dalam berbagai domain khusus seperti pertanian [4], kemacetan lalu lintas kota [5] dan keputusan pengadilan [6]. Topik pertanian memiliki istilah-istilah khusus yang dipahami secara terbatas oleh stakeholder bidang pertanian seperti proses dan prosedur produksi tanaman, metode dan alat pertanian, siklus panen, dan penanganan hama atau penyakit tanaman.…”

Section: Iunclassified

Serverless Named Entity Recognition untuk Teks Instruksional Pertanian Kota

Gelar

Nanda

Bakhrun

2022

JuTISI

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Named Entity Recognition(NER) merupakan bagian dari pengembangan perangkuman dokumen, klasifikasi dan pencarian informasi. Implementasi NER pada domain pertanian, khususnya teks instruksional atau transkripsi video tutorial, akan memudahkan masyarakat umum memahami konsep dan istilah khusus dari kegiatan pertanian kota seperti proses dan prosedur produksi tanaman, metode dan alat pertanian, siklus panen, dan penanganan hama atau penyakit tanaman. Spacy merupakan alat bantu NLP, memiliki dua metode pengembangan model NER, yaitu dengan Toc2Vec dan Transformer. Kedua metode memiliki kelebihan dan kekurangan, yaitu ukuran, performansi dan kecepatan prediksi yang berbeda beda sesuai kebutuhan. Model NER dapat diimplementasikan menjadi aplikasi Serverless, dengan menggunakan pendekatan Fungsional as Services (FaaS) dan Backend as Services (BaaS). Pada penelitian ini telah dikembangkan tiga model NER untuk data teks instruksional pertanian sub topik budidaya tanaman buah. Model berbasis Toc2Vec dengan optimasi efisiensi, Model Toc2Vec dengan optimasi akurasi dan Model berbasis IndoBERT. Model berbasis Transformer memiliki nilai f1-score terbaik sebesar 0.71 disusul Model Toc2Vec Efisiensi sebesar 0.60 dan Model Toc2Vec Efektif dan 0.57. Model Toc2Vec tidak dapat memprediksi entitas numerik dengan baik, Prediksi entitas COUNT, PERIOD dan VERIETAS selalu tertukar. Selain itu Ukuran model berbanding lurus dengan kecepatan prediksi kata per detik, dalam hal ini Model Toc2Vec optimasi efisiensi unggul, model tersebut mudah diimplementasikan menjadi Serverless berbasis FaaS dan BaaS. Fungsionalitas dari Serverless ML telah berhasil diuji menggunakan metode Blackbox.

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“…Meanwhile, Leitner et al in 2019 [12] used machine learning methods like CRF (Conditional Random Fields) and deep learning methods like BiLSTM (Bidirectional Long Short-Term Memory) to extract information using NER on legal documents. Nuranti and Yulianti in 2020 [13] also tried the same method in Indonesian legal documents. Those research [11,12,13] used generated PDF legal documents as their data source, while our study used scanned PDF documents which may have lower accuracy influenced by OCR errors.…”

Section: Related Workmentioning

confidence: 99%

“…Nuranti and Yulianti in 2020 [13] also tried the same method in Indonesian legal documents. Those research [11,12,13] used generated PDF legal documents as their data source, while our study used scanned PDF documents which may have lower accuracy influenced by OCR errors.…”

Section: Related Workmentioning

confidence: 99%

Optical Character Recognition Engines Performance Comparison in Information Extraction

Ramdhani

Budi²,

Purwandari³

2021

IJACSA

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Named Entity Recognition (NER) is often used to acquire important information from text documents as a part of the Information Extraction (IE) process. However, the text documents quality affects the accuracy of the data obtained, especially for text documents acquired involving the Optical Character Recognition (OCR) process, which never reached 100% accuracy. This research tried to examine which OCR engine with the highest performance for IE using NER by comparing three OCR engines (Foxit, PDF2GO, Tesseract) over 8,562 government human resources documents within six document categories, two document structures, and four measurements. Several essential entities such as name, employee ID, document number, document publishing date, employee rank, and family member's name were trying to be extracted automatically from the documents. NER processes were done using Python programming language, and the preprocessing tasks were done separately for Foxit, PDF2GO, and Tesseract. In summary, each OCR engine has its drawbacks and benefit, such as Tesseract has better NER extraction and conversion time with better accuracy but lack in the number of entities acquired.

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Legal Entity Recognition in Indonesian Court Decision Documents Using Bi-LSTM and CRF Approaches

Cited by 12 publications

References 4 publications

A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs

A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs

Serverless Named Entity Recognition untuk Teks Instruksional Pertanian Kota

Optical Character Recognition Engines Performance Comparison in Information Extraction

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