| 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. |
| Approach: | They propose to model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. |
| Outcome: | Empirical results show that the proposed method outperforms transfer learning, multi-task learning, and few-shot learning methods on four datasets. |
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| Challenge: | Existing methods for named entity recognition (NER) use labeled data for both source and target domains. |
| Approach: | They propose to use language modeling as a bridge between NER domains to perform cross-domain and cross-task knowledge transfer. |
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A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
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Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
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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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Improving Named Entity Recognition via Bridge-based Domain Adaptation (2023.findings-acl)
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What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis (D19-1)
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
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Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)
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| Challenge: | Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles. |
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Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)
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| Challenge: | Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains. |
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Structure and Label Constrained Data Augmentation for Cross-domain Few-shot NER (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) tasks require large datasets with accurate annotations that are labor-intensive and time-consuming. |
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Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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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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Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) performance often degrades when applied to target domains that differ from the texts observed during training. |
| Approach: | They propose a method to learn NER models in the absence of labelled data through weak supervision by using a broad spectrum of labelling functions to automatically annotate texts from the target domain. |
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