Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction
arXiv:2402.14521v2 Announce Type: replace Abstract: Standard English and Malaysian English exhibit notable differences, posing challenges for natural language processing (NLP) tasks on Malaysian English. Unfortunately, most of the existing datasets are mainly based on standard English and therefore inadequate for improving NLP tasks in Malaysian English. An experiment using state-of-the-art Named Entity Recognition (NER) solutions on Malaysian English news articles highlights that they cannot handle morphosyntactic variations in Malaysian English. To the best of our knowledge, there is no anno
The proliferation of NLP models and the increasing focus on localized AI applications are driving the need for diverse language datasets.
This development highlights the critical importance of localized data for effective AI deployment in diverse linguistic contexts, especially for generative AI models which often struggle with non-standard English variants.
The creation of a Malaysian English news dataset enables more accurate and relevant AI applications for the Malaysian linguistic landscape, potentially fostering local AI development.
- · Malaysian NLP researchers
- · Malaysian tech companies
- · NLP dataset developers
- · Generic English NLP model providers (without adaptation)
Improved NLP performance for Malaysian English applications such as search, chatbots, and content analysis becomes possible.
This could spur the development of a localized AI ecosystem within Malaysia, reducing reliance on global, standardized English models.
The success in Malaysian English could inspire similar efforts for other non-standard English dialects or less-resourced languages, furthering global AI inclusivity.
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Read at arXiv cs.CL