Papers with NER datasets
A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)
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| Challenge: | Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure. |
| Approach: | They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features. |
| Outcome: | The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics. |
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning (2023.acl-short)
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| Challenge: | Parameter-efficient fine-tuning only optimizes a few task-specific parameters with frozen pre-trained model. |
| Approach: | They propose to optimize a prefix vector inserted into Transformer layers to optimize the prefix . they propose to use a gate mechanism to adjust the prefixed to each layer . |
| Outcome: | The proposed approach improves on the SuperGLUE and NER datasets. |
SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities (2024.naacl-long)
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| Challenge: | Named entity recognition (NER) is a task to identify textual spans that correspond to named entities in the given text. |
| Approach: | They propose a model that can generalize to entities unseen during training and handle noisy annotations. |
| Outcome: | The proposed model outperforms existing methods on both MNER and GMNER benchmarks and is robust and accurate. |
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |