Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work on few-shot named entity recognition (NER) addresses flat entities instead of nested entities. |
| Approach: | They propose a method based on focusing, bridging and prompting for few-shot nested NER without using source domain data. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed several competing models on F1-score by 9.33% on ACE2004, 6.17% on ace2005, 9.40% on GENIA and 5.12% on KBP2017. |
Similar Papers
TECA: A Two-stage Approach with Controllable Attention Soft Prompt for Few-shot Nested Named Entity Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for few-shot nested named entity recognition (NER) ignore relationship between inner and outer entities, which is crucial for fewshot ner. |
| Approach: | They propose a span-based method with a controllable attention soft prompt for few-shot nested named entity recognition (TECA) the span part identification provides possible entity mentions without an extra filtering module. |
| Outcome: | The proposed method outperforms baseline models on four benchmark datasets and outperformed competing models on F1-score by 5.62% on ACE04, 5.11% on ace05, 3.41% on KBP2017 and 0.7% on GENIA on the 10-shot setting. |
Causal Intervention-based Few-Shot Named Entity Recognition (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to perform few-shot named entity recognition are limited and overfitting is caused by the spurious correlation resulting from the bias in selecting a few samples. |
| Approach: | They propose a causal intervention-based few-shot named entity recognition method that blocks the backdoor path between context and label. |
| Outcome: | The proposed method achieves state-of-the-art in a few-shot named entity recognition (NER) task. |
Prompt-Based Metric Learning for Few-Shot NER (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing metric learning methods do not fully incorporate label semantics into modeling. |
| Approach: | They propose a method to largely improve metric learning for few-shot named entity recognition (NER) a pre-defined category is a key natural language understanding task . |
| Outcome: | The proposed method outperforms the previous state-of-the-art (SOTA) method with 16 of 18 settings outperformed previous methods by 9.12% and 34.51% . |
A Streamlined Span-based Factorization Method for Few Shot Named Entity Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing approaches to few-shot named entity recognition require large amounts of labeled data. |
| Approach: | They propose a streamlined span-based factorization method that solves few-shot NER problem . they propose to decompose the span-level alignment problem into several refined procedures . |
| Outcome: | The proposed method achieves an average F1 score improvement of 12 points on the FewNERD dataset and 10 points on SNIPS dataset. |
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Few-shot named entity recognition methods struggle with out-of-domain (OOD) examples due to their reliance on manual labeling for the target domain. |
| Approach: | They propose a framework to enable generalization to an unseen target domain with only a few labeled examples. |
| Outcome: | The proposed framework achieves significant performance improvements on in-domain and cross-domain datasets. |
Few-Shot Named Entity Recognition: An Empirical Baseline Study (2021.emnlp-main)
Copied to clipboard
Jiaxin Huang, Chunyuan Li, Krishan Subudhi, Damien Jose, Shobana Balakrishnan, Weizhu Chen, Baolin Peng, Jianfeng Gao, Jiawei Han
| Challenge: | Existing methods to build named entity recognition systems with limited labeled data are lacking. |
| Approach: | They propose three orthogonal schemes to build named entity recognition systems when labeled data is limited. |
| Outcome: | The proposed NER systems outperform existing methods on few-shot and training-free settings. |
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media. |
| Approach: | They propose a few-shot named entity recognition system based on nearest neighbor learning and structured inference. |
| Outcome: | The proposed method improves F1 scores on standard few-shot NER evaluation tasks by 6% to 16% relative to previous methods. |
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
Copied to clipboard
| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
A Novel Three-stage Framework for Few-shot Named Entity Recognition (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for Named Entity Recognition (NER) rely on labeled data, but data scarcity is a major challenge. |
| Approach: | They propose a framework for Few-shot Named Entity Recognition that can learn from limited labeled data and generalize to new domains. |
| Outcome: | The proposed framework surpasses existing methods on several benchmarks. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |