Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models. |
| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
| Outcome: | The proposed method achieves better performance on the BioRED dataset. |
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SpanNER: Named Entity Re-/Recognition as Span Prediction (2021.acl-long)
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| Challenge: | Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. |
| Approach: | They experimentally implement 154 named entity recognition models on 11 datasets and show that span prediction can serve as a system combiner to re-recognize named entities from different systems’ outputs. |
| Outcome: | The proposed model can be used to re-recognize named entities from different systems’ outputs. |
A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image. |
| Approach: | They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs. |
| Outcome: | The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods. |
A Label-Aware Autoregressive Framework for Cross-Domain NER (2022.findings-naacl)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
| Approach: | They propose a framework that borrows entity information from the source domain to enhance NER in the target domain. |
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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. |
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Neural Architectures for Nested NER through Linearization (P19-1)
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| Challenge: | a nested named entity recognition (NER) is a set of entities that can overlap and be labeled with more than one label. |
| Approach: | They propose two neural network architectures for nested named entity recognition . they propose to model nesting entities as multilabels and predict a sequence-to-sequence problem . |
| Outcome: | The proposed methods outperform the state-of-the-art on four corpora . the proposed models also improve on the recently published contextual embeddings . |
NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)
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| Challenge: | Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding. |
| Approach: | They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks. |
SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing. |
| Approach: | They propose a method which introduces a Named Entity Head (NEH) prediction task to SpanNER and performs multi-task learning together with task of span classification. |
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CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) tasks require large labeled datasets to perform well. |
| Approach: | They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them. |
| Outcome: | The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets . |
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)
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| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages (2026.findings-eacl)
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| 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. |