Towards Malay named entity recognition: an open-source dataset and a multi-task framework
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 buil...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2159014 |
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| author | Yingwen Fu Nankai Lin Zhihe Yang Shengyi Jiang |
| author_facet | Yingwen Fu Nankai Lin Zhihe Yang Shengyi Jiang |
| author_sort | Yingwen Fu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ba6bc85e83d45f29ae6a035d964e73f |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-0ba6bc85e83d45f29ae6a035d964e73f2025-08-20T03:17:14ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2022.21590142159014Towards Malay named entity recognition: an open-source dataset and a multi-task frameworkYingwen Fu0Nankai Lin1Zhihe Yang2Shengyi Jiang3Guangdong University of Foreign StudiesGuangdong University of TechnologyGuangdong University of Foreign StudiesGuangdong University of Foreign StudiesNamed 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.http://dx.doi.org/10.1080/09540091.2022.2159014malaynamed entity recognitiondatasetmulti-task learningbi-revision |
| spellingShingle | Yingwen Fu Nankai Lin Zhihe Yang Shengyi Jiang Towards Malay named entity recognition: an open-source dataset and a multi-task framework Connection Science malay named entity recognition dataset multi-task learning bi-revision |
| title | Towards Malay named entity recognition: an open-source dataset and a multi-task framework |
| title_full | Towards Malay named entity recognition: an open-source dataset and a multi-task framework |
| title_fullStr | Towards Malay named entity recognition: an open-source dataset and a multi-task framework |
| title_full_unstemmed | Towards Malay named entity recognition: an open-source dataset and a multi-task framework |
| title_short | Towards Malay named entity recognition: an open-source dataset and a multi-task framework |
| title_sort | towards malay named entity recognition an open source dataset and a multi task framework |
| topic | malay named entity recognition dataset multi-task learning bi-revision |
| url | http://dx.doi.org/10.1080/09540091.2022.2159014 |
| work_keys_str_mv | AT yingwenfu towardsmalaynamedentityrecognitionanopensourcedatasetandamultitaskframework AT nankailin towardsmalaynamedentityrecognitionanopensourcedatasetandamultitaskframework AT zhiheyang towardsmalaynamedentityrecognitionanopensourcedatasetandamultitaskframework AT shengyijiang towardsmalaynamedentityrecognitionanopensourcedatasetandamultitaskframework |