| Challenge: | Named entity recognition models often encounter over-confidence issues . boundary smoothing is a method that re-assigns entity probabilities from annotated spans to the surrounding ones . |
| Approach: | They propose a method for regularizing entity probabilities from annotated spans to the surrounding ones. |
| Outcome: | The proposed method achieves better than or competitive with previous state-of-the-art systems on well-known benchmarks. |
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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. |
DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks. |
| Approach: | They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans. |
| Outcome: | The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets. |
Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
| Outcome: | The proposed model can access external gazetteers while avoiding the effort to design hand-crafted features. |
A Boundary Offset Prediction Network for Named Entity Recognition (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing . span-based methods assign entity types to text spans, resulting in imbalanced sample space . |
| Approach: | They propose a method that predicts boundary offsets between candidate and nearest spans . the method integrates entity type and span representations to generate type-aware boundary offset . |
| Outcome: | The proposed method outperforms existing methods on eight widely-used NER datasets. |
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
| Outcome: | The proposed model trains twice as fast as a CRF with statistically insignificant differences in F1 . the proposed model is open source and can be used in PyTorch and TensorFlow. |
Noisy-Labeled NER with Confidence Estimation (2021.naacl-main)
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| Challenge: | Recent studies in deep learning have shown significant progress in named entity recognition (NER) . however, most existing works assume clean data annotation, while real-world data typically involve a large amount of noises. |
| Approach: | They propose a confidence estimation approach for named entity recognition using noisy labels using local and global independence assumptions. |
| Outcome: | The proposed method marginalizes out labels of low confidence with a CRF model and integrates it into a self-training framework for boosting performance. |
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network (2021.emnlp-main)
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| Challenge: | Existing methods to label data and identify entities require large amounts of manually annotated texts for training supervised models. |
| Approach: | They propose a dictionary extension method which extracts new entities through the type expanded model. |
| Outcome: | The proposed method outperforms state-of-the-art supervised systems on different types of datasets and surpasses supervised models. |
DiFiNet: Boundary-Aware Semantic Differentiation and Filtration Network for Nested Named Entity Recognition (2024.acl-long)
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| Challenge: | Existing approaches to Named Entity Recognition focus on identifying non-nested entities, but there is no explicit guidance for boundary detection. |
| Approach: | They propose a Boundary-aware Semantic Differentiation and Filtration Network for nested NER that leverages a biaffine attention mechanism to generate a span representation matrix. |
| Outcome: | Extensive experiments on three benchmark datasets demonstrate the proposed model yields a new state-of-the-art performance. |
Learning Named Entity Tagger using Domain-Specific Dictionary (D18-1)
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| Challenge: | Existing methods to build reliable named entity recognition systems require large amounts of manually-labeled training data. |
| Approach: | They propose a revised fuzzy CRF layer to handle tokens with multiple possible labels to address noisy distant supervision. |
| Outcome: | The proposed model can handle tokens with multiple possible labels under the traditional framework and improves on the existing model with a new Tie or Break scheme. |