| Challenge: | Existing methods for named entity recognition and mention detection ignore nested entities along with any semantic relations between them. |
| Approach: | They propose a recurrent neural network-based approach to handle named entity recognition and nested entity mention detection simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three standard datasets. |
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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 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. |
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 . |
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (C18-1)
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| Challenge: | Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. |
| Approach: | They propose to use recurrent neural networks to generate NERs over characters, sub-words and/or word embeddings to improve named entity recognition. |
| Outcome: | The proposed architectures are better than those based on feature engineering and other supervised or semi-supervised learning algorithms. |
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. |
Joint Learning of Named Entity Recognition and Entity Linking (P19-2)
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| Challenge: | Named entity recognition and entity linking are two fundamentally related tasks . most approaches focus on the mention detection part, assuming the correct mentions have been detected . |
| Approach: | They perform joint learning of named entity recognition and entity linking to leverage their relatedness. |
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Neural Segmental Hypergraphs for Overlapping Mention Recognition (D18-1)
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| Challenge: | Existing models that assume that mentions are non-overlapping spans in text may not perform well in practice. |
| Approach: | They propose a segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. |
| Outcome: | The proposed representation achieves state-of-the-art performance in three benchmark datasets annotated with overlapping mentions. |
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 . |
| Outcome: | The proposed method closes 40% of the gap to full nested supervision on a Russian benchmark with 29 entity types where 21% of entities are nest. |
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. |