# Parallel-Based Corpus Annotation for Malay Health Documents  
[PERSON]  
1 Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, UKM,  
Bangi 43000, Selangor, Malaysia; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (L.Q.Z. )1 Faculty of Industrial Technology, Universitas Pembangunan National \"Veteran\" Yogyakarta,  
Yogyakarta 55283, Indonesia1  
[PERSON]  
1 Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, UKM,  
Bangi 43000, Selangor, Malaysia; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (L.Q.Z. )1 Faculty of Industrial Technology, Universitas Pembangunan National \"Veteran\" Yogyakarta,  
Yogyakarta 55283, Indonesia1  
[PERSON]  
1 Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, UKM,  
Bangi 43000, Selangor, Malaysia; [EMAIL_ADDRESS] (S.S.); [EMAIL_ADDRESS] (L.Q.Z. )1 Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, UKM,  
resources would enable researchers to develop more accurate and effective NER models specific to the Malay language, improving the overall quality of Malay NLP applications. Currently, domain-specific applications primarily focus on the specific context and often do not extend to other languages with diverse morphological and syntactic structures [4]. Therefore, the development of a standard Malay language corpus and machine learning algorithms tailored to the language is essential. This would enable the expansion of NLP applications to encompass a wider range of domains and promote cross-linguistic research and development. By investing in creating a comprehensive annotated corpus and advancing machine learning algorithms for Malay NLP, researchers can unlock the full potential of NER and other NLP tasks in the Malay language [5]. This will not only contribute to the growth of the field but also facilitate the development of innovative applications that cater to the unique linguistic characteristics of Malay. The growth of health-related information in the Malay language necessitates the development of NLP tools and resources tailored to the Malay-speaking community. However, the existing NER tools primarily focus on basic entity types, such as person, organization, and location, and often do not support the Malay language. Moreover, it should be noted that the field of identifying syntax and semantics in the Malay language lacks the abundance of tools and resources that are readily available in English [6]. This scarcity poses a significant challenge in accurately performing named entity recognition (NER) tasks in Malay health documents. These challenges highlight the need for specialized NER models and resources specifically tailored to the Malay language and the health domain. In addressing this challenge, leveraging parallel corpora, which consist of aligned texts in English and Malay, emerges as the most suitable solution. By utilizing parallel corpora, we can leverage the existing tools and resources for English NER and adapt them effectively to the Malay language, facilitating the identification of named entities in Malay health documents. This approach maximizes the available resources and enables the development of robust NER models specifically tailored to the Malay language. The primary objective of this research is to address the challenges of healthcare NER in the Malay language by constructing an annotated corpus and facilitating the development of tailored NER algorithms for the healthcare domain. By creating a comprehensive annotated corpus and advancing machine learning algorithms for Malay NLP, this research aims to unlock the full potential of NER and other NLP tasks in the Malay language, ultimately improving information extraction, analysis, and understanding in the healthcare sector. In this paper, the research on building an annotated corpus for Malay health documents is presented, focusing on named entity recognition (NER). The paper is divided into four sections.