| 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. |
Similar Papers
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. |
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. |
| Outcome: | Experiments on benchmark datasets ACE-2005, GENIA and JNLPBA show that the proposed framework performs better than state-of-the-art models. |
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 . |
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. |
Nested Named Entity Recognition with Span-level Graphs (2022.acl-long)
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| Challenge: | Named entity recognition is one of the major subtasks of information extraction for extracting categorized named entities from unstructured text. |
| Approach: | They propose to use retrieval-based span-level graphs to connect spans and entities in the training data based on n-gram features to integrate information of similar neighbor entities into the span representation. |
| Outcome: | The proposed method achieves general improvements on all three benchmarks and special superiority on low frequency entities. |
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 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. |
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. |