Named Entity Recognition Ner On Conll 2003
评估指标
F1
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | F1 |
|---|---|
| named-entity-recognition-with-bidirectional | 91.62 |
| promptner-prompt-locating-and-typing-for | 92.41 |
| a-deep-neural-network-model-for-the-task-of | 91.22 |
| empower-sequence-labeling-with-task-aware | 91.24 |
| subregweigh-effective-and-efficient | 93.81 |
| subregweigh-effective-and-efficient | 94.2 |
| focusing-on-possible-named-entities-in-active | 93.6 |
| learning-better-internal-structure-of-words | 91.64 |
| neural-reranking-for-named-entity-recognition | 91.62 |
| transfer-learning-for-sequence-tagging-with | 91.26 |
| cloze-driven-pretraining-of-self-attention | 93.5 |
| evaluating-the-utility-of-hand-crafted | 92.29 |
| semi-supervised-sequence-modeling-with-cross | 92.61 |
| a-prism-module-for-semantic-disentanglement | 91.8 |
| named-entity-recognition-architecture | 93.28 |
| transformer-based-named-entity-recognition-1 | 93.69 |
| end-to-end-sequence-labeling-via-bi | 91.21 |
| generalizing-natural-language-analysis-1 | 92.2 |
| gollie-annotation-guidelines-improve-zero | 93.1 |
| locate-and-label-a-two-stage-identifier-for | 92.94 |
| semi-supervised-sequence-modeling-with-cross | 92.61 |
| robust-multilingual-part-of-speech-tagging | 91.56 |
| dice-loss-for-data-imbalanced-nlp-tasks | 93.33 |
| efficient-contextualized-representation | 92.03 |
| luke-deep-contextualized-entity | 94.3 |
| hierarchical-contextualized-representation | 93.37 |
| ncrf-an-open-source-neural-sequence-labeling | 91.35 |
| baseline-needs-more-love-on-simple-word | 86.28 |
| autoregressive-structured-prediction-with | 93.8 |
| improved-differentiable-architecture-search | 93.47 |
| promptner-prompt-locating-and-typing-for | 93.08 |
| 190910148 | 92.4 |
| harnessing-deep-neural-networks-with-logic | 91.18 |
| multi-grained-named-entity-recognition | 92.28 |
| tener-adapting-transformer-encoder-for-name | 92.62 |
| a-unified-generative-framework-for-various | 93.24 |
| a-unified-mrc-framework-for-named-entity | 93.04 |
| robust-multilingual-named-entity-recognition | 91.36 |
| pack-together-entity-and-relation-extraction | 94.0 |
| hybrid-semi-markov-crf-for-neural-sequence | 91.38 |
| diffusionner-boundary-diffusion-for-named | 92.78 |
| 模型 42 | 91.74 |
| hierarchical-contextualized-representation | 91.96 |
| named-entity-recognition-architecture | 93.82 |
| towards-improving-neural-named-entity | 92.75 |
| autoregressive-structured-prediction-with | 94.1 |
| exploring-cross-sentence-contexts-for-named | 93.74 |
| automated-concatenation-of-embeddings-for-1 | 94.6 |
| learning-from-noisy-labels-for-entity-centric | 94.22 |
| evaluating-the-utility-of-hand-crafted | 91.87 |
| sentence-state-lstm-for-text-representation | 91.57 |
| robust-lexical-features-for-improved-neural | 91.73 |
| joint-learning-of-named-entity-recognition | 92.43 |
| boundary-smoothing-for-named-entity-1 | 93.65 |
| automated-concatenation-of-embeddings-for-1 | 93.64 |
| neural-architectures-for-named-entity | 90.94 |
| contextual-string-embeddings-for-sequence | 93.09 |
| grn-gated-relation-network-to-enhance | 92.34 |
| inferner-an-attentive-model-leveraging-the | 93.76 |
| flert-document-level-features-for-named | 94.09 |
| parallel-instance-query-network-for-named | 92.87 |
| named-entity-recognition-architecture | 88.63 |
| neural-architectures-for-nested-ner-through-1 | 93.38 |
| improving-named-entity-recognition-by | 93.35 |
| deep-contextualized-word-representations | 92.22 |
| named-entity-recognition-as-dependency | 93.5 |
| gcdt-a-global-context-enhanced-deep | 93.47 |
| improving-named-entity-recognition-by | 93.85 |
| unified-named-entity-recognition-as-word-word | 93.07 |
| crossweigh-training-named-entity-tagger-from | 93.43 |
| variational-sequential-labelers-for-semi-1 | 84.7 |
| long-short-term-memory-with-dynamic-skip | 91.56 |
| gcdt-a-global-context-enhanced-deep | 91.96 |