SmartSpanNER: Making SpanNER Robust in Low Resource Scenarios (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing. |
| Approach: | They propose a method which introduces a Named Entity Head (NEH) prediction task to SpanNER and performs multi-task learning together with task of span classification. |
| Outcome: | The proposed method improves the robustness of SpanNER in low resource scenarios on the CoNLL03, Few-NERD, GENIA and ACE05 benchmark datasets. |
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SpanNER: Named Entity Re-/Recognition as Span Prediction (2021.acl-long)
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| Challenge: | Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. |
| Approach: | They experimentally implement 154 named entity recognition models on 11 datasets and show that span prediction can serve as a system combiner to re-recognize named entities from different systems’ outputs. |
| Outcome: | The proposed model can be used to re-recognize named entities from different systems’ outputs. |
GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks (2022.acl-srw)
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| Challenge: | Named Entity Recognition (NER) uses sequence labelling and span classification to identify entities. |
| Approach: | They propose a framework that uses Graph Neural Networks to enrich the span representation to reduce the number of overlapping spans during prediction. |
| Outcome: | The proposed framework reduces the number of overlapping spans while maintaining competitive metric performance. |
Towards Building More Robust NER datasets: An Empirical Study on NER Dataset Bias from a Dataset Difficulty View (2023.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context. |
| Approach: | They propose to de-bias NER datasets by altering entity-context distribution . they also validate the feasibility of the proposed de-bianking techniques . |
| Outcome: | The proposed methods can be applied to different models and improve existing models. |
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. |
SLENDER: Structured Outputs for SLM-based NER in Low-Resource Englishes (2025.acl-industry)
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| Challenge: | Named Entity Recognition (NER) for low-resource variants of English remains challenging, as most models are trained on datasets predominantly focused on American or British English. |
| Approach: | They propose a new output format for Named Entity Recognition (NER) that achieves a three-fold reduction in inference time compared to JSON format. |
| Outcome: | The proposed output format achieves a three-fold reduction in inference time on average compared to JSON format, which is widely used for structured outputs. |
ScdNER: Span-Based Consistency-Aware Document-Level Named Entity Recognition (2023.emnlp-main)
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| Challenge: | Named entity recognition (NER) is an important task for many natural language processing applications. |
| Approach: | They propose to fuse global features of tokens via word-based key-value memory to produce documentlevel encoding for token label prediction. |
| Outcome: | The proposed model can produce consistent and consistent predictions on word level with reduced impact of non-entity sequences and adaptive global feature fusion. |
Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders (2025.acl-short)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task . supervised learning or full fine-tuning remains essential for high performance NER models. |
| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
| Outcome: | The proposed method achieves better performance on the BioRED dataset. |
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework (2021.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a language understanding task that requires large amounts of in-domain labeled data to perform well. |
| Approach: | They propose a framework which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. |
| Outcome: | The proposed method brings 10%, 23% and 26% improvements over baselines in few-shot learning, domain transfer and zero-shot settings respectively. |
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
| Outcome: | The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively. |
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. |