A Rigorous Study on Named Entity Recognition: Can Fine-tuning Pretrained Model Lead to the Promised Land? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a fundamental task of information extraction. |
| Approach: | They propose to perform randomization tests on standard NER benchmarks to examine name regularity, mention coverage and context diversity. |
| Outcome: | The proposed model performs better on standard NER benchmarks than other models on open datasets. |
Similar Papers
Coarse-to-Fine Pre-training for Named Entity Recognition (2020.emnlp-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a task of discovering information entities and identifying their corresponding categories. |
| Approach: | They propose a NER-specific framework to inject coarse-to-fine named entity knowledge into pre-trained models by using a remote supervision strategy. |
| Outcome: | The proposed framework achieves significant improvements against several pre-trained base-lines, demonstrating its effectiveness in label-few and low-resource scenarios. |
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition (2021.tacl-1)
Copied to clipboard
| Challenge: | Name Regularity Bias is a problem in NER models that use contextual information to predict the type of an ambiguous entity. |
| Approach: | They propose a model-agnostic training method that adds learnable adversarial noise to some entity mentions to improve their accuracy. |
| Outcome: | The proposed method outperforms feature-based models on name regularity bias . it adds learnable adversarial noise to some entity mentions, leading to gains . |
Random Splitting Negatively Impacts NER Evaluation: Quantifying and Eliminating the Overestimation of NER Performance (2025.findings-acl)
Copied to clipboard
| Challenge: | Named entity recognition models are evaluated on their ability to identify entity mentions in text. |
| Approach: | They propose a method to reduce the amount of entity contamination in NER datasets by a minimum cut algorithm. |
| Outcome: | The proposed method minimizes train-test entity leakage while ensuring near zero entity contamination. |
Improving Model Generalization: A Chinese Named Entity Recognition Case Study (2021.acl-short)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a fundamental building block for various downstream natural language processing tasks due to the ambiguous word boundaries and complex composition. |
| Approach: | They propose to resample entities within the same category to encourage a model to leverage both name and context knowledge in the training process. |
| Outcome: | The proposed method significantly improves a model’s ability to detect unseen entities, especially for company, organization and position categories. |
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning pre-trained language models are limited . we propose a few-shot fine-uning framework for NER . |
| Approach: | They propose a few-shot fine-tuning framework for named entity recognition (NER) they propose three new types of tokens, "is-entity", "which-type" and "bracket" |
| Outcome: | The proposed framework improves on pre-trained language models on several benchmark datasets. |
Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)
Copied to clipboard
| 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. |
Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023? (2023.acl-long)
Copied to clipboard
| Challenge: | NER models trained on 20-year-old test set may not perform well on modern data. |
| Approach: | They evaluate the generalization of over 20 different models trained on the CoNLL-2003 dataset . they find no evidence of performance degradation in pre-trained Transformers . |
| Outcome: | The proposed model generalizations show that some models generalize well on new data while others do not. |
CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English (2024.lrec-main)
Copied to clipboard
| 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. |
Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets (2023.emnlp-main)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) often suffers from insufficient labeled data when the number of annotations exceeds several tens of labels. |
| Approach: | They propose a model with a fine-to- coarse mapping matrix to leverage hierarchical structure explicitly. |
| Outcome: | The proposed model outperforms both K-shot learning and supervised learning methods when dealing with a small number of fine-grained annotations. |
What do we really know about State of the Art NER? (2022.lrec-1)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) is a well researched task and widely used in real world NLP scenarios. |
| Approach: | They perform a broad evaluation of Named Entity Recognition using a popular dataset that takes into consideration various text genres and sources constituting the dataset at hand. |
| Outcome: | The proposed models perform on a popular dataset and generate six new adversarial test sets through small perturbations in the original test set, replacing select entities while retaining the context. |