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Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?

arXiv 2505.16814 ↗First posted 22 May 2025cs.CLcited by 0

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Abstract (arXiv)

Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data augmentation for increasing the amount of low-resource labeled data is a common practice. In this paper, we explore the role of synthetic data in the context of multilingual, low-resource NER, considering 11 languages from diverse language families. Our results suggest that synthetic data does in fact hold promise for low-resource language NER, though we see significant variation between languages.

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References · 19

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  12. 2022Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languagescited by 28
7 more
  1. 2023Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmarkcited by 26
  2. 2022Toward More Meaningful Resources for Lower-resourced Languagescited by 14
  3. 2020Sources of Transfer in Multilingual Named Entity Recognitioncited by 9
  4. 2024CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPcited by 9
  5. 2019What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysiscited by 4
  6. 2022DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognitioncited by 3
  7. 2024Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fieldscited by 3

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