# Parallel-Based Corpus Annotation for Malay Health Documents
## 2 Background and Related Work
[PERSON] et al. [17] proposed a rules-based Malay NER system that achieved a recall value of 92.13%, a precision value of 90.23%, and an F-score of 91.05%. [PERSON] et al. [18] combined the fuzzy c-means and K-nearest neighbors algorithm methods, resulting in an overall success rate of 95.24% for entity recognition in Malay crime data. [PERSON] et al. [19] developed the Mi-NER system using a probabilistic approach with a linear-chain CRF machine learning technique. Finally, [PERSON] et al. [20] extracted nouns from Malay classical documents with a 77.61% chance of identifying a noun, while [PERSON] et al. [21] employed a rule-based approach with manually created dictionaries and achieved a recall of 94.44%, precision of 85%, and an F-score of 89.47%. While several studies have been conducted on Malay NER, it is important to note that the current state of resources for the Malay language is still limited. For example, the lack of an annotated corpus remains a challenge for developing a reliable Malay NER system. Additionally, the availability of completed Malay noun lists or dictionaries is limited, requiring manual review by language experts. These limitations highlight the need for further research and resource development in the field of Malay NER. While the current landscape of Malay NER research has primarily focused on crime and news resources, there is a need for research in diverse domains. By expanding the scope of research, we can develop more comprehensive Malay NER systems that can be applied to a wide range of applications and industries. This will require the development of resources specific to these domains and the exploration of new methodologies and techniques. Developing Malay NER systems specifically for the health domain is crucial for improving information extraction, analysis, and understanding in the healthcare sector. Accurately extracting and classifying named entities in Malay health documents can enhance the accuracy of NER systems and ultimately contribute to advancements in healthcare services and research. By developing comprehensive and domain-specific resources for Malay NER in the health domain, we can ensure that NER systems are tailored to the unique characteristics of the Malay language and the specific terminology and nuances present in the health domain. The lack of annotated text corpora in Malay named entity recognition (NER) is a significant challenge in developing supervised learning algorithms. This lack of resources highlights the broader issue of limited Malay natural language processing (NLP) resources, including the absence of credible Malay NER systems [22]. A comprehensive NER corpus is crucial for training and evaluating NER models, enabling researchers and practitioners to develop more effective algorithms and gain a deeper understanding of named entities in the Malay language [23]. The scarcity of annotated text in Malay NER hinders progress in developing accurate and robust NER systems for the language. The lack of credible Malay NER systems further emphasizes the need for comprehensive NLP resources tailored to the Malay language. A high-quality NER corpus would facilitate the development of more effective algorithms and enable researchers and practitioners to gain deeper insights into the characteristics and patterns of named entities in Malay [24]. Future research should prioritize expanding the range of domains covered by Malay NER systems and developing more comprehensive and established resources for the Malay language. Addressing these challenges is crucial to making NLP technology accessible and beneficial to the Malay-speaking community. Advancements in Malay NER can significantly improve the language processing capabilities in Malay and empower researchers, practitioners, and users to leverage the potential of NLP technology. Furthermore, the development of domain-specific Malay NER systems, such as in finance, law, or education, would broaden the applicability and impact of NLP in the Malay language.