Analysis of document retrieval for online public administrative procedure services.

Public Administrative Procedures (APs) are processes, implementation methods, documents, and requirements or conditions prescribed by gov­ernment agencies or authorized individuals to ad­dress specific tasks related to individuals or orga­nizations. However, organizations and citizens still face challenges in easily and conveniently accessing information and public administrative services. This paper investigates advanced techniques to address the challenges of document retrieval for public admin­istrative services. We implement a hybrid retrieval process, combining traditional retrieval models such as TF-IDF and BM25 with modern models such as SBERT and fine-tuning models. Results demonstrate that combining these models significantly enhances retrieval performance. The ensemble model of BM25 and finetuned SBERT achieved the highest F2 score, indicating superior effectiveness in information re­trieval.

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Publication Details

Journal
Journal on Information Technologies & Communications
Published
2024-10-07
DOI
https://doi.org/10.32913/mic-ict-research-vn.v2024.n2.1297
Primary Topic
Data Mining Algorithms and Applications
Type
article
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article

Analysis of document retrieval for online public administrative procedure services.

Dinh-Dien La, Khanh-Van Tran
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Journal on Information Technologies & Communications
Data Mining Algorithms and Applications
article

Analysis of document retrieval for online public administrative procedure services.

Dinh-Dien La, Khanh-Van Tran
article en

Abstract

Public Administrative Procedures (APs) are processes, implementation methods, documents, and requirements or conditions prescribed by gov­ernment agencies or authorized individuals to ad­dress specific tasks related to individuals or orga­nizations. However, organizations and citizens still face challenges in easily and conveniently accessing information and public administrative services. This paper investigates advanced techniques to address the challenges of document retrieval for public admin­istrative services. We implement a hybrid retrieval process, combining traditional retrieval models such as TF-IDF and BM25 with modern models such as SBERT and fine-tuning models. Results demonstrate that combining these models significantly enhances retrieval performance. The ensemble model of BM25 and finetuned SBERT achieved the highest F2 score, indicating superior effectiveness in information re­trieval.

Journal on Information Technologies & Communications
Peace, Justice and strong institutions
Openalex Percentile: Top 73%
Data Mining Algorithms and Applications
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