| 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 . |
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Merge and Label: A Novel Neural Network Architecture for Nested NER (P19-1)
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| Challenge: | Named entity recognition (NER) is one of the best studied tasks in natural language processing. |
| Approach: | They propose a neural network architecture that merges tokens and/or entities into nested entities and labels them independently. |
| Outcome: | The proposed approach achieves state-of-the-art F1 of 74.6 and improves with contextual embeddings to 82.4. |
Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. |
| Approach: | They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. |
| Outcome: | The proposed model achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models. |
A Neural Layered Model for Nested Named Entity Recognition (N18-1)
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| Challenge: | Entity mentions embedded in longer entity mentions are referred to as nested entities due to the properties of natural language. |
| Approach: | They propose a neural model that dynamically stacks flat NER layers to identify nested entities by capturing sequential context representation with bidirectional long-term memory. |
| Outcome: | The proposed model outperforms state-of-the-art feature-based systems on nested NER, achieving 74.7% and 72.2% on GENIA and ACE2005 datasets, respectively in terms of F-score. |
Deep Exhaustive Model for Nested Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition (NER) is a task of finding entities with specific semantic types such as Protein, Cell, and RNA in text. |
| Approach: | They propose a deep neural model for nested named entity recognition . they enumerate all possible regions or spans as potential entity mentions . |
| Outcome: | The proposed model outperforms state-of-the-art models on nested and flat NER . it achieves 77.1% and 78.4% respectively in terms of F-score, without external knowledge resources. |
Named Entity Recognition With Parallel Recurrent Neural Networks (P18-2)
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| Challenge: | Named entity recognition is an important element of natural language understanding . a shift in focus has been on designing better neural architectures for solving NER . |
| Approach: | They propose a new architecture for named entity recognition that uses multiple LSTM units instead of a single LStm component. |
| Outcome: | The proposed architecture achieves state-of-the-art on the CoNLL 2003 NER dataset . |
Nested Named Entity Recognition as Single-Pass Sequence Labeling (2025.findings-emnlp)
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| Challenge: | nested named entity recognition is a sequence labeling task that can be trained using any off-the-shelf sequence labelling library. |
| Approach: | They use prior work that linearizes constituency structures to create a nested named entity recognition task. |
| Outcome: | The proposed method reduces the complexity of the predicted nested entity recognition problem to a simple token classification task. |
A Boundary-aware Neural Model for Nested Named Entity Recognition (D19-1)
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| Challenge: | Existing methods for named entity recognition ignore nested entities . a boundary-aware neural model can locate entities precisely by detecting boundaries . |
| Approach: | They propose a boundary-aware neural model for nested named entity recognition which leverages entity boundaries to predict entity categorical labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on GENIA dataset . it captures dependencies of entity boundaries and categorical labels, which helps to improve identifying entities. |
Exploring Nested Named Entity Recognition with Large Language Models: Methods, Challenges, and Insights (2024.emnlp-main)
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| Challenge: | Named entity recognition (NER) is a challenging task in natural language processing . nested NER requires sophisticated techniques to identify entities within entities . |
| Approach: | They investigate the application of Large Language Models (LLMs) to nested NER . they find methodologies from previous work are less effective . |
| Outcome: | The proposed methods outperform BERT-based models in nested NER tasks . however, they do not outperformed the existing models on the GENIA dataset . |
An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition (2023.acl-short)
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| Challenge: | Named entity recognition (NER) is the task to detect and classify entity spans in text. |
| Approach: | They propose to use Convolutional Neural Network to model spatial relations in NER . they use three commonly used nested NER datasets to compare their results . |
| Outcome: | The proposed model outperforms several proposed methods with the same pre-trained encoders in three nested NER datasets. |
A Unified MRC Framework for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition is divided into nested NER and flat NER depending on whether entities are nesting. |
| Approach: | They propose to formulate named entity recognition task as machine reading comprehension task instead of sequence labeling problem . |
| Outcome: | The proposed framework achieves vast amount of performance boost over current models on nested and flat NER datasets. |