| 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. |
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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. |
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. |
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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 . |
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Pyramid: A Layered Model for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task. |
| Approach: | They propose a pyramid-like layered model for Nested Named Entity Recognition . token or text region embeddings are recursively inputted into L flat NER layers . |
| Outcome: | The proposed model achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE 2005, GENIA, and NNE. |
Learning Nested Named Entity Recognition from Flat Annotations (2026.eacl-srw)
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| Challenge: | Named entity recognition (NER) requires expensive multi-level annotation. |
| Approach: | They evaluate four approaches to learning nested structure from flat annotations alone . on NEREL, a Russian benchmark, they find the best method achieves 26.37% inner F1 . |
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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. |
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. |
Hierarchical Region Learning for Nested Named Entity Recognition (2020.findings-emnlp)
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| Challenge: | Existing methods to recognize entities recursively from innermost to outermost are based on brute force and two-stage paradigms, often leading to cascaded errors. |
| Approach: | They propose a hierarchical region learning framework to automatically generate a tree hierarchy of candidate regions with nearly linear complexity and incorporate structure information into the region representation for better classification. |
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Bipartite Flat-Graph Network for Nested Named Entity Recognition (2020.acl-main)
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| Challenge: | Existing models only consider the unidirectional delivery of information from innermost layers to outer ones, but instead focus on nested entities. |
| Approach: | They propose a bipartite flat-graph network (BiFlaG) for nested named entity recognition (NER) the bipartites are bidirectional LSTM and graph convolutional network (GCN) they first use the entities recognized by the flat NER module to construct an entity graph . |
| Outcome: | The proposed model outperforms existing models on three standard nested NER datasets. |
Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
| Approach: | They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact. |
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