Malaysian English News Decoded: A Linguistic Resource for Named Entity
  and Relation Extraction
Mohan Raj Chanthran Lay-Ki Soon Huey Fang Ong Bhawani Selvaretnam
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 annotated dataset available to improvise the model. To address
these issues, we constructed a Malaysian English News (MEN) dataset, which
contains 200 news articles that are manually annotated with entities and
relations. We then fine-tuned the spaCy NER tool and validated that having a
dataset tailor-made for Malaysian English could improve the performance of NER
in Malaysian English significantly. This paper presents our effort in the data
acquisition, annotation methodology, and thorough analysis of the annotated
dataset. To validate the quality of the annotation, inter-annotator agreement
was used, followed by adjudication of disagreements by a subject matter expert.
Upon completion of these tasks, we managed to develop a dataset with 6,061
entities and 3,268 relation instances. Finally, we discuss on spaCy fine-tuning
setup and analysis on the NER performance. This unique dataset will contribute
significantly to the advancement of NLP research in Malaysian English, allowing
researchers to accelerate their progress, particularly in NER and relation
extraction. The dataset and annotation guideline has been published on Github.