Towards Malay named entity recognition: an open-source dataset and a multi-task framework

Abstract

Named entity recognition (NER) is a key component of many natural language processing (NLP) applications. The majority of advanced research, however, has not been widely applied to low-resource languages represented by Malay due to the data-hungry problem. In this paper, we present a system for building a Malay NER dataset (MS-NER) of 20,146 sentences through labelled datasets of homologous languages and iterative optimisation. Additionally, we propose a Multi-Task framework, namely MTBR, to integrate boundary information more effectively for NER. Specifically, boundary detection is treated as an auxiliary task and an enhanced Bidirectional Revision module with a gated ignoring mechanism is proposed to undertake conditional label transfer. This can reduce error propagation by the auxiliary task. We conduct extensive experiments on Malay, Indonesian, and English. Experimental results show that MTBR could achieve competitive performance and tends to outperform multiple baselines. The constructed dataset and model would be made available to the public as a new, reliable benchmark for Malay NER.

Keywords:

• Malay
• named entity recognition
• dataset
• multi-task learning
• Bi-revision

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://word.tips/100-most-spoken-languages/

2 https://dl.acm.org/doi/proceedings/10.1145/2824864

3 https://huggingface.co/malay-huggingface/bert-base-bahasa-cased

4 https://pytorch.org/

5 https://github.com/huggingface/transformers

6 https://huggingface.co/bert-base-cased

Additional information

Funding

This work was supported by the Science and Technology Program of Guangzhou [grant number 202002030227] and GDUFS Yunshan Office on Public Opinion, one of the Internet culture demonstration projects of higher education institutions in Guangdong.