PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition (2021.emnlp-main)
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| Challenge: | Existing approaches to Named Entity Recognition (NER) are limited in labeled resources and domain shift. |
| Approach: | They propose a progressive domain adaptation knowledge distillation approach to adapt high-resource domains to low-resourced target domains by employing three components to achieve superior domain adaptability. |
| Outcome: | The proposed approach can adapt high-resource domains to low-resourced target domains even if they are diverse in terms and writing styles. |
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| Challenge: | Existing methods for named entity recognition focus on augmenting in-domain data in low-resource scenarios where annotated data is limited. |
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| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
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| Challenge: | Existing methods for named entity recognition use pre-training language models to represent words, leading to entity type misclassification. |
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| Challenge: | Empirical results show that our method outperforms a series of transfer learning, multitask learning, and few-shot learning methods due to the data scarcity in the real-world scenario. |
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Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
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