| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |
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Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a key task in NLP to find mentions of named entities and classify them into predefined categories. |
| Approach: | They investigated the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. |
| Outcome: | The data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context. |
| Approach: | They propose to de-bias NER datasets by altering entity-context distribution . they also validate the feasibility of the proposed de-bianking techniques . |
| Outcome: | The proposed methods can be applied to different models and improve existing models. |
Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
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Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
| Challenge: | Modern large language models perform in-context learning, where query- answer demonstrations are shown before the final query. |
| Approach: | They propose to use in-context learning to prompt queries before they are answered . they find that the choice of demonstrations can affect model performance . |
| Outcome: | The proposed model performance improves on named entity replacements across three reasoning tasks and two popular LLMs. |
Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)
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| Challenge: | Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features. |
| Approach: | They propose to embed words and entity types into a low-dimensional vector space and compute a feature vector representing each word offline. |
| Outcome: | The proposed representations outperform existing models and achieve state-of-the-art performance. |
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training (2021.emnlp-main)
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| Challenge: | Named entity recognition models require abundant high-quality annotations to train . distant supervision may induce incomplete and noisy labels, making supervised learning ineffective. |
| Approach: | They propose a noise-robust learning scheme for training named entity recognition models using only distantly-labeled data and a self-training method that uses contextualized augmentations created by pre-trained language models. |
| Outcome: | The proposed method outperforms existing supervised NER models on three datasets by significant margins. |
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
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| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
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. |
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English (2024.lrec-main)
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| Challenge: | a glass ceiling for named entity recognition systems has been suggested for 2021 . however, the performance of the most popular NER benchmarks has plateaued since then . we investigate what NER models are still struggling with . |
| Approach: | They perform a fine-grained evaluation of the model outputs by adding document annotations to the CoNLL-03 English dataset to identify lingering errors. |
| Outcome: | The proposed model is able to correct errors and guide future work. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |