# Parallel-Based Corpus Annotation for Malay Health Documents
## 2 Background and Related Work  
The development of named entity recognition (NER) systems for the Malay language has been the focus of numerous researchers. In this section, we will explore the current state of Malay NER research, including the domains that have been predominantly studied and the methodologies and results of various studies. Additionally, we will discuss the existing resources available for the Malay language and highlight the need for further research and resource development in diverse domains beyond crime and news. Existing datasets for the health domain are readily available in several languages, such as English [7; 8; 9; 10], Chinese [11; 12], and Indonesian [13; 14]. [PERSON] et al. [13] discussed the identification of medical entities using the conditional random field (CRF) approach, which aims to extract health information, such as on diseases, symptoms, treatments, and medicines, from online health forum discussions in Indonesian. However, although Indonesian and Malaysian have much in common, they are two different languages. When it comes to Malay named entity recognition (NER) in the health domain, the available resources are primarily focused on domains like crime and news. It is important to note that named entity recognition (NER) systems designed for the public domain may not yield optimal performance when applied to health documents due to the domain-specific terminology and language variations. The specialized vocabulary and linguistic nuances present in the health domain require tailored NER models and resources to accurately extract and classify named entities in Malay health documents. Malay, being an agglutinative language, poses challenges for named entity recognition due to its unique morphological structures [15]. For example, the formation of words via affixation and compounding can result in variations in word forms and make it difficult to accurately identify named entities. Additionally, the presence of linguistic nuances, such as honorifics and honorific markers, further adds to the complexity of recognizing named entities in Malay. These challenges highlight the need for specialized NER models and resources that can effectively handle the intricacies of the Malay language in the health domain. Efforts should be made to curate and annotate large-scale health corpora in Malay, encompassing a wide range of health-related topics and sources. This would enable the development and evaluation of specialized NER models tailored to the unique characteristics of the Malay language and the specific domain of health. By expanding the available resources for Malay NER in the health domain and promoting research on health literacy, we can enhance the accuracy and applicability of NER systems, ultimately improving information extraction, analysis, and understanding in the healthcare sector. This, in turn, can contribute to advancements in healthcare services, research, and the overall well-being of Malay speakers. Numerous researchers have dedicated their efforts to developing named entity recognition (NER) systems specifically for the Malay language, with a particular focus on Malay NER. However, the current landscape of Malay NER research is predominantly limited to the domains of crime and news resources. In the section discussing various studies on Malay NER, we provide an overview of several studies conducted on Malay NER, examining their methodologies, outcomes, and the existing state of resources available for the Malay language. By exploring these studies, we aim to gain insights into the progress, challenges, and opportunities in the field of Malay NER and highlight the need for further research and resource development in diverse domains beyond crime and news. Several studies have been conducted on Malay NER, each employing different methodologies and achieving varying levels of success. For example, [PERSON] et al. [16] created a crime news corpus and manually annotated entities, achieving a recall value of 78.67%, a precision of 71.11%, and an F-measure of 74.7%.