Application of named entity recognition method for Indonesian datasets: a review

Abstract

A name entity (NE) is a proper name that designates a person, location, or organization. For humans, named entity recognition (NER) is a straightforward process insofar as many named entities are self-names, and most of them have initial capital letters and can be easily recognized, but it is very difficult for machines. This study discusses research trends in the application of NER to Indonesian datasets, particularly as it concerns certain tasks, datasets, methods/techniques, and entity labels. By conducting a systematic literature review (SLR) and bibliometric analysis with VOSviewer, this article hopes to provide opportunities for adopting old methods, combining models from previous research, and even proposing new methods. In addition, the motivation for doing SLR at NER is to look for new strategies in the supervision of financial technology (Fintech). If machines can find illegal Fintech entities on social media and online news, it can help the government to block these illegal Fintech entities. To this end, this study provides an overview of research trends in applying the NER method to Bahasa Indonesia (Indonesian) datasets, including the extraction of news articles, the monitoring of floods, and traffic.