ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (2023.acl-long)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a task of detecting linguistically complex named entities in low-context text. |
| Approach: | They propose a keyword-based augmentation approach to address the context-entity mismatch issue in complex name recognition (NER) they use selective masking to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entity. |
| Outcome: | The proposed approach outperforms baseline methods on monolingual, cross-lingual, and multilingual complex NER in various low-resource settings. |
Similar Papers
Data Augmentation for Cross-Domain Named Entity Recognition (2021.emnlp-main)
Copied to clipboard
| 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. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
Copied to clipboard
| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) exhibit remarkable adaptability across domains, but they are often not suitable for structured knowledge extraction tasks such as named entity recognition (NER). |
| Approach: | They propose a method that instructs LLMs to self-reflect on the specific domain and generates domain-relevant attributes for creating attribute-rich training data. |
| Outcome: | The proposed method produces NER datasets in domains with domain-relevant attributes and generates entity terms and NER context data around these entities. |
Targeted Augmentation for Low-Resource Event Extraction (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for low-resource information extraction struggle to strike a balance between weak augmentation and drastic augmentation. |
| Approach: | They propose a data augmentation paradigm that uses back validation and targeted augmentation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
| Outcome: | The proposed paradigm produces augmented examples with enhanced diversity, polarity, accuracy, and coherence. |
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing NER benchmarks lack quality annotations, resulting in poor performance. |
| Approach: | They propose a frequency-based iterative approach that leverages self-training and a dual-threshold mechanism to enhance inference confidence. |
| Outcome: | The proposed approach improves NER performance on three datasets with a high number of missing annotations. |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
Copied to clipboard
Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
Copied to clipboard
| 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. |
NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
| Outcome: | The proposed model matches or surpasses existing models in NER tasks . the proposed model is based on a large, silver-annotated corpus of 4 million paragraphs . |
Constrained Labeled Data Generation for Low-Resource Named Entity Recognition (2021.findings-acl)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) in lowresource languages has been a challenge for years . Existing methods suffer from low quality of annotated data in target language . |
| Approach: | They propose a method that uses projected annotations to generate pseudo supervised data with a transformer language model and a constrained beam search. |
| Outcome: | The proposed method achieves state-of-the-art or competitive performance in low-resource languages. |
Knowledge-Augmented Language Model and Its Application to Unsupervised Named-Entity Recognition (N19-1)
Copied to clipboard
| Challenge: | Current language models are unable to efficiently model entity names observed in text providing insufficient context. |
| Approach: | They propose to augment a traditional model with an external knowledge base to model entity names observed in text. |
| Outcome: | The proposed model improves on a Named Entity Recognition (NER) task by requiring no additional information such as named entity tags. |