| Challenge: | Existing approaches to entity recognition are based on class labels in source and target domains, and many NER corpora only annotate a small number of categories. |
| Approach: | They replicate and extend several past studies on transfer learning for entity recognition. |
| Outcome: | The proposed methods perform better when there is more labeled target data. |
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Transfer Learning for Named-Entity Recognition with Neural Networks (L18-1)
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| Challenge: | Existing approaches to named-entity recognition (NER) require additional lead time for developing and fine-tuning the rules. |
| Approach: | They propose to transfer an ANN model trained on a large labeled dataset to another dataset with a limited number of labels to improve upon the state-of-the-art results for patient note de-identification. |
| Outcome: | The proposed model can be transferred to a dataset with a limited number of labels, and improves on the state-of-the-art results on patient note de-identification. |
Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences (2024.naacl-long)
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| Challenge: | Existing models for named entity recognition fail in scientific domains such as biomedicine and chemistry. |
| Approach: | They propose a model to transfer knowledge from the biomedical domain to the target domain . they use pseudo labeling and contrastive learning to enhance discrimination . |
| Outcome: | The proposed model outperforms baseline models by up to 5% . the proposed model is based on a biomedical domain model and a chemical domain model . |
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. |
Cross-lingual Transfer Learning for Japanese Named Entity Recognition (N19-2)
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| Challenge: | a recent study focuses on bootstrapping named entity models from English to Japanese . TL is a technique that overcomes linguistic differences between the target and source languages . |
| Approach: | They propose to use a deep neural network model to transfer weights between languages . they also propose a novel approach that romanizes a portion of the Japanese input . |
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An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification (C18-1)
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| Challenge: | a recent study compares semi-supervised learning methods with bootstrapping methods . semi-semi-supervised methods reduce the amount of semantic drift introduced by iterative approaches . |
| Approach: | They propose to adapt three semi-supervised representation learning methods to an information extraction task . they show that all methods outperform state-of-the-art semi-representation learning methods . |
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Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
| Outcome: | The proposed model improves a type-based Named Entity Recognition (NER) training corpus and predicts and annotates new types in a test corpus. |
Weakly Supervised Attention Networks for Entity Recognition (D19-1)
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| Challenge: | Existing approaches to entity recognition require large amounts of token-level data, which can be expensive and cumbersome to obtain. |
| Approach: | They propose a weakly supervised model that can be annotated at word level from a corpus containing binary presence/absence labels. |
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Transfer Learning in Natural Language Processing (N19-5)
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| Challenge: | supervised machine learning is based on learning in isolation, a single predictive model for a task using a dataset. |
| Approach: | They present an overview of modern transfer learning methods in natural language processing . they review examples and case studies on how models can be integrated and adapted . |
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DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections (2021.eacl-main)
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Yury Zemlyanskiy, Sudeep Gandhe, Ruining He, Bhargav Kanagal, Anirudh Ravula, Juraj Gottweis, Fei Sha, Ilya Eckstein
| Challenge: | Using pre-trained models, we learn to jointly predict words and entities from multiple text sources without any human supervision. |
| Approach: | They propose to learn rich self-supervised entity representations from large amounts of associated text. |
| Outcome: | The proposed models outperform baseline models on downstream tasks in the TV-Movies domain, and scale to very large corpora. |
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. |
| 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. |