Papers with named entity recognition tasks
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. |
Nested Named Entity Recognition via Second-best Sequence Learning and Decoding (2020.tacl-1)
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| Challenge: | Named entity recognition (NER) is the task of identifying text spans associated with proper names and classifying them according to their semantic class such as person or organization. |
| Approach: | They propose a method that treats the tag sequence for nested entities as the second best path within the span of their parent entity. |
| Outcome: | The proposed method achieves F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE 2005, and GENIA datasets. |
Self-Training with Differentiable Teacher (2022.findings-naacl)
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| Challenge: | Existing methods for self-training are interpreted as teacher-student frameworks, where the teacher generates pseudo-labels and the student makes predictions. |
| Approach: | They propose a differentiable self-training method that treats teacher-student as a Stackelberg game where a leader is always in a more advantageous position than a follower. |
| Outcome: | The proposed model outperforms existing methods on semi- and weakly-supervised learning tasks on semi and weak supervised tasks. |
CNNBiF: CNN-based Bigram Features for Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Named entity recognition tasks require a self-attention mechanism with unconstrained length that fails to capture local dependencies. |
| Approach: | They propose a joint training objective which better captures the semantics of words corresponding to the same entity by augmenting the objective with a group-consistency loss component. |
| Outcome: | The proposed model achieves a test F1 of 93.98 with a single transformer model. |
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization (2025.coling-main)
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| Challenge: | Existing methods to reduce the adverse effect of annotation errors are time-consuming because they require many trained models to detect errors. |
| Approach: | They propose a method that uses a tokenization technique called subword regularization to simulate multiple error detection models for detecting errors. |
| Outcome: | The proposed method performs weighting weighting four to five times faster than existing methods and improves in document classification and named entity recognition tasks. |
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER (D19-1)
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| Challenge: | Contextual word embeddings have demonstrated state-of-the-art performance on various NLP tasks. |
| Approach: | They propose to use adversarial learning to improve upon multilingual BERT's zero-resource cross-lingual performance by aligning embeddings of English documents and their translations. |
| Outcome: | The multilingual version of BERT performs surprisingly well in cross-lingual settings, even when only labeled English data is used to finetune the model. |
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data (2024.lrec-main)
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| Challenge: | Clinical data in hospitals are unstructured and therefore need to be extracted from medical reports to conduct clinical studies. |
| Approach: | They propose a dedicated French biomedical model based on a public French biomedicine dataset. |
| Outcome: | The proposed model improves 2.54 points of F1-score on biomedical named entity recognition tasks. |
KALA: Knowledge-Augmented Language Model Adaptation (2022.naacl-main)
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| Challenge: | Pre-trained language models (PLMs) have proved to be effective on various natural language understanding tasks. |
| Approach: | They propose a domain adaption framework which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. |
| Outcome: | The proposed framework outperforms adaptive pre-training on question answering and named entity recognition tasks on multiple datasets across different domains. |
Learning How to Active Learn by Dreaming (P19-1)
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| Challenge: | Recent active learning methods are limited when the data distribution of learning problems vary. |
| Approach: | They propose a wake-and-dream-based active learning method that learns the AL policy directly on the target domain of interest by using wake and dream cycles. |
| Outcome: | The proposed method improves on cross-domain and cross-lingual tasks. |
Large Language Models Relearn Removed Concepts (2024.findings-acl)
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| Challenge: | a recent study has shown that models can re-acquire pruned concepts after editing . however, it remains unclear whether models can recover such concepts after retraining . |
| Approach: | They evaluate concept relearning in large language models by tracking concept saliency and similarity in pruned neurons during retraining for named entity recognition tasks. |
| Outcome: | The results show that models can re-acquire pruned concepts after pruning . they also show that they can blend old and new concepts in individual neurons . |
A Class-Rebalancing Self-Training Framework for Distantly-Supervised Named Entity Recognition (2023.findings-acl)
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| Challenge: | Distant supervision reduces the reliance on human annotation in named entity recognition tasks. |
| Approach: | They propose a class-rebalancing self-training framework for improving distantly-supervised named entity recognition by using a flexible threshold and a hybrid pseudo label. |
| Outcome: | The proposed model achieves state-of-the-art on five flat and two nested datasets and compares with other methods on the same dataset. |