| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
| Approach: | They propose to use existing neural architectures to adapt to new domains without retraining . they propose to add adaptation layers to existing neural models to minimize re-training based on source data. |
| Outcome: | The proposed approach significantly outperforms state-of-the-art methods on social media domains. |
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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. |
| Approach: | They propose a neural architecture to transform data from high-resource to low-resourced domains by learning the patterns in the text that differentiate them. |
| Outcome: | The proposed approach improves on high-resource domain representations over high- and low-resourced domains. |
Cross-Domain NER using Cross-Domain Language Modeling (P19-1)
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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. |
| Outcome: | The proposed method extracts domain differences from cross-domain LM contrast, allowing unsupervised domain adaptation while giving state-of-the-art results among supervised domain adapters. |
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. |
| Approach: | They propose a simple and efficient neural architecture for cross-lingual named entity recognition models. |
| Outcome: | The proposed model achieves competitive performance with the state-of-the-art on two transferable factors: sequential order and multilingual embedding. |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
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| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
| Approach: | They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition. |
| Outcome: | The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms. |
Improving Named Entity Recognition via Bridge-based Domain Adaptation (2023.findings-acl)
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| Challenge: | Existing methods for named entity recognition use pre-training language models to represent words, leading to entity type misclassification. |
| Approach: | They propose a model-agnostic framework called MoCL for cross-domain named entity recognition to refine the original representations and combine it with two distinct cross- domain NER methods and two pre-training language models to explore its generalization ability. |
| Outcome: | The proposed framework is model-agnostic and can be used to generalize and refine existing models. |
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition (2021.acl-long)
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| Challenge: | Experimental results show that crowdsourced annotations are highly effective under supervised conditions. |
| Approach: | They propose an annotator-aware representation learning model that is inspired by domain adaptation methods which attempt to capture effective domain-alike features. |
| Outcome: | The proposed model is highly effective on a benchmark dataset and achieves state-of-the-art performance with only a very small scale of expert annotations. |
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge (2023.eacl-main)
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| Challenge: | Named entity recognition (NER) is costly because of lack of training data and domain experts. |
| Approach: | They propose a self-adaptive neural model that retrieves external knowledge from unstructured text to learn the usages of entities that have not been learned well. |
| Outcome: | The proposed model outperforms strong baselines on cross-neuro-ner datasets by 2.35 points in F1 metric. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |